diff --git a/404.html b/404.html
index 4fdb03f..a2d0aa4 100644
--- a/404.html
+++ b/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://sydneybiox.github.io/wSIR/index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -66,7 +66,7 @@ <h1>Page not found (404)</h1>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/articles/index.html b/articles/index.html
index 575f300..d122593 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -49,7 +49,7 @@ <h3>All vignettes</h3>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/articles/wSIR_vignette.html b/articles/wSIR_vignette.html
index 4b6f7c8..4ff218e 100644
--- a/articles/wSIR_vignette.html
+++ b/articles/wSIR_vignette.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -250,32 +250,32 @@ <h3 id="parameter-study">Parameter Study<a class="anchor" aria-label="anchor" hr
 and slice combinations (which correspond to wSIR).</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="va">optim_obj</span> <span class="op">=</span> <span class="fu"><a href="../reference/exploreWSIRParams.html">exploreWSIRParams</a></span><span class="op">(</span>exprs <span class="op">=</span> <span class="va">sample1_exprs</span>, </span>
+<span><span class="va">optim_obj</span> <span class="op">=</span> <span class="fu"><a href="../reference/exploreWSIRParams.html">exploreWSIRParams</a></span><span class="op">(</span>X <span class="op">=</span> <span class="va">sample1_exprs</span>, </span>
 <span>                              coords <span class="op">=</span> <span class="va">sample1_coords</span>, </span>
 <span>                              alpha_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">.5</span>, <span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">4</span>,<span class="fl">8</span><span class="op">)</span>, </span>
 <span>                              slice_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">10</span>,<span class="fl">15</span><span class="op">)</span>,</span>
 <span>                              nrep <span class="op">=</span> <span class="fl">20</span>, metric <span class="op">=</span> <span class="st">"CD"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Time difference of 3.836403 secs</span></span>
-<span><span class="co">## Time difference of 4.253446 secs</span></span>
-<span><span class="co">## Time difference of 4.422207 secs</span></span>
-<span><span class="co">## Time difference of 3.272594 secs</span></span>
-<span><span class="co">## Time difference of 4.135383 secs</span></span>
-<span><span class="co">## Time difference of 4.446194 secs</span></span>
-<span><span class="co">## Time difference of 3.394855 secs</span></span>
-<span><span class="co">## Time difference of 4.115742 secs</span></span>
-<span><span class="co">## Time difference of 4.265517 secs</span></span>
-<span><span class="co">## Time difference of 2.989446 secs</span></span>
-<span><span class="co">## Time difference of 3.536304 secs</span></span>
-<span><span class="co">## Time difference of 3.744084 secs</span></span>
-<span><span class="co">## Time difference of 3.000982 secs</span></span>
-<span><span class="co">## Time difference of 3.266891 secs</span></span>
-<span><span class="co">## Time difference of 3.711689 secs</span></span>
-<span><span class="co">## Time difference of 3.043132 secs</span></span>
-<span><span class="co">## Time difference of 3.222942 secs</span></span>
-<span><span class="co">## Time difference of 3.594974 secs</span></span></code></pre>
+<pre><code><span><span class="co">## Time difference of 13.9127 secs</span></span>
+<span><span class="co">## Time difference of 13.72888 secs</span></span>
+<span><span class="co">## Time difference of 14.08977 secs</span></span>
+<span><span class="co">## Time difference of 12.80965 secs</span></span>
+<span><span class="co">## Time difference of 14.11709 secs</span></span>
+<span><span class="co">## Time difference of 14.00368 secs</span></span>
+<span><span class="co">## Time difference of 12.99858 secs</span></span>
+<span><span class="co">## Time difference of 13.8238 secs</span></span>
+<span><span class="co">## Time difference of 13.7851 secs</span></span>
+<span><span class="co">## Time difference of 12.24654 secs</span></span>
+<span><span class="co">## Time difference of 13.42864 secs</span></span>
+<span><span class="co">## Time difference of 13.662 secs</span></span>
+<span><span class="co">## Time difference of 12.32729 secs</span></span>
+<span><span class="co">## Time difference of 13.384 secs</span></span>
+<span><span class="co">## Time difference of 13.77942 secs</span></span>
+<span><span class="co">## Time difference of 12.11121 secs</span></span>
+<span><span class="co">## Time difference of 13.42403 secs</span></span>
+<span><span class="co">## Time difference of 13.60149 secs</span></span></code></pre>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span><span class="op">-</span><span class="va">a</span></span></code></pre></div>
-<pre><code><span><span class="co">## Time difference of 1.1131 mins</span></span></code></pre>
+<pre><code><span><span class="co">## Time difference of 4.029568 mins</span></span></code></pre>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">optim_obj</span><span class="op">$</span><span class="va">plot</span></span></code></pre></div>
 <p><img src="wSIR_vignette_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
@@ -388,7 +388,7 @@ <h3 id="umap-on-low-dimensional-embedding">UMAP on low-dimensional embedding<a c
 on their expression for those genes.</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">umap_coords</span> <span class="op">=</span> <span class="fu"><a href="../reference/generateUmapFromWSIR.html">generateUmapFromWSIR</a></span><span class="op">(</span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span><span class="op">)</span></span>
-<span><span class="va">umap_plots</span> <span class="op">=</span> <span class="fu"><a href="../reference/plotUmapFromWSIR.html">plotUmapFromWSIR</a></span><span class="op">(</span>exprs <span class="op">=</span> <span class="va">sample1_exprs</span>,</span>
+<span><span class="va">umap_plots</span> <span class="op">=</span> <span class="fu"><a href="../reference/plotUmapFromWSIR.html">plotUmapFromWSIR</a></span><span class="op">(</span>X <span class="op">=</span> <span class="va">sample1_exprs</span>,</span>
 <span>                              umap_coords <span class="op">=</span> <span class="va">umap_coords</span>,</span>
 <span>                              highest_genes <span class="op">=</span> <span class="va">top_genes_obj</span>,</span>
 <span>                              n_genes <span class="op">=</span> <span class="fl">6</span><span class="op">)</span></span>
@@ -431,24 +431,38 @@ <h3 id="single-sample-spatial-dataset-single-sample-single-cell-dataset">Single-
 data:</p>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">sample2_low_dim_exprs</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] 2986    9</span></span></code></pre>
+<pre><code><span><span class="co">## [1] 2986   20</span></span></code></pre>
 <p>Observe some of sample 2’s low-dimensional gene expression data:</p>
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">sample2_low_dim_exprs</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##           [,1]       [,2]        [,3]         [,4]       [,5]        [,6]</span></span>
-<span><span class="co">## [1,] 1.2044363 -0.3790739 -1.72818610 -1.797653672 -0.8684785 -0.74469382</span></span>
-<span><span class="co">## [2,] 0.5738578  0.4719606  0.26341538 -0.341280096 -0.4274574  0.34500227</span></span>
-<span><span class="co">## [3,] 1.2749872 -0.8741444 -1.34429322  0.009172746  0.4562988  0.42206043</span></span>
-<span><span class="co">## [4,] 1.7326508 -0.3322124 -0.55148621  0.732668048 -0.2272738  0.30675261</span></span>
-<span><span class="co">## [5,] 2.1501634 -0.8665235  0.04544417  0.779635209 -0.4277316  0.17938548</span></span>
-<span><span class="co">## [6,] 1.2074795 -1.1035173  0.51632770  1.048309253 -1.0852001 -0.01835717</span></span>
-<span><span class="co">##          [,7]      [,8]       [,9]</span></span>
-<span><span class="co">## [1,] 3.437305 0.5718475 -0.4848100</span></span>
-<span><span class="co">## [2,] 1.304516 1.7813543  0.6194540</span></span>
-<span><span class="co">## [3,] 2.313171 0.0540894 -0.3177639</span></span>
-<span><span class="co">## [4,] 2.269962 0.5799911 -1.2385839</span></span>
-<span><span class="co">## [5,] 0.899609 1.5397581  0.1789930</span></span>
-<span><span class="co">## [6,] 1.244870 0.5720243  0.4845666</span></span></code></pre>
+<pre><code><span><span class="co">##           [,1]       [,2]       [,3]       [,4]       [,5]       [,6]</span></span>
+<span><span class="co">## [1,] 1.0930341 -0.1443871 -1.5090562  1.5873578 -1.3890866  0.3799610</span></span>
+<span><span class="co">## [2,] 0.4680595  0.5327497 -0.3728135  1.0015734  0.8920196  0.1513758</span></span>
+<span><span class="co">## [3,] 1.2711455 -0.7397591 -1.4112419 -0.8839003  0.2468790  0.3832880</span></span>
+<span><span class="co">## [4,] 1.7531946 -0.1143880 -0.7064318 -0.3602051  0.4724328 -0.2961441</span></span>
+<span><span class="co">## [5,] 2.2381875 -0.5924558 -0.3165188  0.2141844  0.7782123 -0.2621179</span></span>
+<span><span class="co">## [6,] 1.3299499 -0.8290736  0.6474762  0.6397734  0.5299646 -0.9353155</span></span>
+<span><span class="co">##            [,7]       [,8]        [,9]      [,10]       [,11]      [,12]</span></span>
+<span><span class="co">## [1,] -3.4678998 0.56681244 -0.81551059  1.1852559 -0.73526941 -1.0785081</span></span>
+<span><span class="co">## [2,] -1.0017729 1.95474647 -0.43692515 -0.1603876 -0.15958819 -0.4427703</span></span>
+<span><span class="co">## [3,] -2.3845440 0.08795112  1.16277476  0.2721857  0.28169183  0.4604845</span></span>
+<span><span class="co">## [4,] -2.2187255 0.65176353  0.20339372  1.2128839  1.07096226  0.4317445</span></span>
+<span><span class="co">## [5,] -0.5669189 1.84121180  0.06402206 -0.3221178  0.02488601  0.3517457</span></span>
+<span><span class="co">## [6,] -1.0554684 1.07326688 -0.42296176 -0.3972216 -0.72847203  0.7158252</span></span>
+<span><span class="co">##            [,13]      [,14]        [,15]       [,16]      [,17]       [,18]</span></span>
+<span><span class="co">## [1,]  0.22645766  0.7414184  0.885021243  1.74928451  0.4172434 -0.36172912</span></span>
+<span><span class="co">## [2,] -0.11743827 -1.3097013  0.335409580 -0.19737044  1.9184451  0.06779531</span></span>
+<span><span class="co">## [3,] -0.09524678 -0.7460559 -0.583083970  0.25165178  0.6701605  0.43165415</span></span>
+<span><span class="co">## [4,] -0.04954717 -1.0375261 -0.005446275 -0.59153455  0.4787165  0.06904808</span></span>
+<span><span class="co">## [5,]  1.03117637 -0.4426845 -0.023429569  0.03480782 -0.1772584 -0.21056960</span></span>
+<span><span class="co">## [6,]  0.35505985 -0.2426742  1.098536792  0.38752300  0.1341204  0.42348720</span></span>
+<span><span class="co">##           [,19]       [,20]</span></span>
+<span><span class="co">## [1,]  0.2994094  0.71759073</span></span>
+<span><span class="co">## [2,] -0.3500336 -0.06717946</span></span>
+<span><span class="co">## [3,]  0.6338821  0.32693292</span></span>
+<span><span class="co">## [4,]  0.6517039  0.23217160</span></span>
+<span><span class="co">## [5,] -1.2366838  0.41684787</span></span>
+<span><span class="co">## [6,] -0.9978965  0.58964001</span></span></code></pre>
 <p>This low-dimensional gene expression data can then be used for any
 later tasks which would benefit from a low-dimensional embedding of the
 gene expression data for all the samples, rather than just the gene
@@ -477,7 +491,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <span></span>
 <span><span class="va">sample3_low_dim_exprs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/projectWSIR.html">projectWSIR</a></span><span class="op">(</span>wsir <span class="op">=</span> <span class="va">wsir_obj_samples12</span>, newdata <span class="op">=</span> <span class="va">sample3_exprs</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">sample3_low_dim_exprs</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] 4607    7</span></span></code></pre>
+<pre><code><span><span class="co">## [1] 4607   29</span></span></code></pre>
 <p>This low-dimensional matrix can then be used for downstream tasks
 which would benefit from a low-dimensional embedding of sample 3’s gene
 expression matrix that contains information about each cell’s location.
@@ -497,8 +511,8 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <span>                                k <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="va">knn_classification_object</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] Presomitic mesoderm Dermomyotome        Presomitic mesoderm</span></span>
-<span><span class="co">## [4] Spinal cord         Spinal cord         Low quality        </span></span>
+<pre><code><span><span class="co">## [1] Presomitic mesoderm Presomitic mesoderm Presomitic mesoderm</span></span>
+<span><span class="co">## [4] Presomitic mesoderm Presomitic mesoderm Presomitic mesoderm</span></span>
 <span><span class="co">## 24 Levels: Allantois Anterior somitic tissues ... Surface ectoderm</span></span></code></pre>
 <p>In the above code, we use the wSIR scores as input for a simple
 KNN-based cell type classification tool. We print the tail of the
@@ -510,7 +524,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## R version 4.4.1 (2024-06-14)</span></span>
 <span><span class="co">## Platform: x86_64-pc-linux-gnu</span></span>
-<span><span class="co">## Running under: Ubuntu 22.04.4 LTS</span></span>
+<span><span class="co">## Running under: Ubuntu 22.04.5 LTS</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Matrix products: default</span></span>
 <span><span class="co">## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 </span></span>
@@ -531,7 +545,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <span><span class="co">## other attached packages:</span></span>
 <span><span class="co">##  [1] class_7.3-22       umap_0.2.10.0      vctrs_0.6.5        doBy_4.6.22       </span></span>
 <span><span class="co">##  [5] Rfast_2.1.0        RcppParallel_5.1.9 RcppZiggurat_0.1.6 Rcpp_1.0.13       </span></span>
-<span><span class="co">##  [9] ggplot2_3.5.1      magrittr_2.0.3     wSIR_0.1.2         BiocStyle_2.32.1  </span></span>
+<span><span class="co">##  [9] ggplot2_3.5.1      magrittr_2.0.3     wSIR_0.1.3         BiocStyle_2.32.1  </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## loaded via a namespace (and not attached):</span></span>
 <span><span class="co">##  [1] gtable_0.3.5         xfun_0.47            bslib_0.8.0         </span></span>
@@ -542,7 +556,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <span><span class="co">## [16] compiler_4.4.1       stringr_1.5.1        textshaping_0.4.0   </span></span>
 <span><span class="co">## [19] microbenchmark_1.5.0 munsell_0.5.1        codetools_0.2-20    </span></span>
 <span><span class="co">## [22] htmltools_0.5.8.1    sass_0.4.9           yaml_2.3.10         </span></span>
-<span><span class="co">## [25] pillar_1.9.0         pkgdown_2.1.0        jquerylib_0.1.4     </span></span>
+<span><span class="co">## [25] pillar_1.9.0         pkgdown_2.1.1        jquerylib_0.1.4     </span></span>
 <span><span class="co">## [28] tidyr_1.3.1          MASS_7.3-60.2        openssl_2.2.1       </span></span>
 <span><span class="co">## [31] BiocParallel_1.38.0  cachem_1.1.0         boot_1.3-30         </span></span>
 <span><span class="co">## [34] RSpectra_0.16-2      Deriv_4.1.6          tidyselect_1.2.1    </span></span>
@@ -553,7 +567,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 <span><span class="co">## [49] utf8_1.2.4           broom_1.0.6          withr_3.0.1         </span></span>
 <span><span class="co">## [52] scales_1.3.0         backports_1.5.0      rmarkdown_2.28      </span></span>
 <span><span class="co">## [55] modelr_0.1.11        reticulate_1.39.0    askpass_1.2.0       </span></span>
-<span><span class="co">## [58] ragg_1.3.3           png_0.1-8            evaluate_0.24.0     </span></span>
+<span><span class="co">## [58] ragg_1.3.3           png_0.1-8            evaluate_1.0.0      </span></span>
 <span><span class="co">## [61] knitr_1.48           rlang_1.1.4          glue_1.7.0          </span></span>
 <span><span class="co">## [64] BiocManager_1.30.25  jsonlite_1.8.8       R6_2.5.1            </span></span>
 <span><span class="co">## [67] systemfonts_1.1.0    fs_1.6.4</span></span></code></pre>
@@ -571,7 +585,7 @@ <h3 id="multi-sample-spatial-dataset-single-sample-single-cell-dataset">Multi-sa
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/articles/wSIR_vignette_files/figure-html/unnamed-chunk-4-1.png b/articles/wSIR_vignette_files/figure-html/unnamed-chunk-4-1.png
index b556d38..38febc9 100644
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index b655d4d..4b187f2 100644
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diff --git a/articles/wSIR_vignette_files/figure-html/unnamed-chunk-8-1.png b/articles/wSIR_vignette_files/figure-html/unnamed-chunk-8-1.png
index b879722..010f526 100644
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diff --git a/authors.html b/authors.html
index ecab33d..d7a13bb 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,16 +60,14 @@ <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/sydneybiox/wSIR/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>Woollard M, Nghiem L, Ghazanfar S (2024).
-<em>wSIR: Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data</em>.
-R package version 0.1.2,
-https://sydneybiox.github.io/wSIR/, <a href="https://sydneybiox.github.io/wSIR">https://sydneybiox.github.io/wSIR</a>.
+<em>wSIR: Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data</em>.
+R package version 0.1.3, <a href="https://sydneybiox.github.io/wSIR">https://sydneybiox.github.io/wSIR</a>.
 </p>
       <pre>@Manual{,
-  title = {wSIR: Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data},
+  title = {wSIR: Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data},
   author = {Max Woollard and Linh Nghiem and Shila Ghazanfar},
   year = {2024},
-  note = {R package version 0.1.2,
-        https://sydneybiox.github.io/wSIR/},
+  note = {R package version 0.1.3},
   url = {https://sydneybiox.github.io/wSIR},
 }</pre>
     </div>
@@ -83,7 +81,7 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/index.html b/index.html
index 6369574..89af53e 100644
--- a/index.html
+++ b/index.html
@@ -5,12 +5,12 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<title>Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data • wSIR</title>
+<title>Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data • wSIR</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
-<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data">
 <meta name="description" content="Weighted Sliced Inverse Regression (wSIR) is a supervised dimension reduction algorithm for spatial transcriptomics gene expression data. For a provided gene expression matrix and dataframe of each cell's spatial coordinates, wSIR creates a low-dimensional representation of the gene expression data that preserves the ability to predict spatial coordinates that was present in the gene expression data. Furthermore, wSIR provides interpretable loadings which allow for projection of new single-cell gene expression data into a low-dimensional space which preserves the spatial information present in the gene expression data.">
 <meta property="og:description" content="Weighted Sliced Inverse Regression (wSIR) is a supervised dimension reduction algorithm for spatial transcriptomics gene expression data. For a provided gene expression matrix and dataframe of each cell's spatial coordinates, wSIR creates a low-dimensional representation of the gene expression data that preserves the ability to predict spatial coordinates that was present in the gene expression data. Furthermore, wSIR provides interpretable loadings which allow for projection of new single-cell gene expression data into a low-dimensional space which preserves the spatial information present in the gene expression data.">
 </head>
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -107,7 +107,7 @@ <h2 data-toc-skip>Developers</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/pkgdown.js b/pkgdown.js
index 9757bf9..1a99c65 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -152,3 +152,11 @@ async function searchFuse(query, callback) {
   });
   });
 })(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/pkgdown.yml b/pkgdown.yml
index 1273fc1..d56a6c3 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,9 +1,9 @@
 pandoc: 3.1.11
-pkgdown: 2.1.0
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   wSIR_vignette: wSIR_vignette.html
-last_built: 2024-09-17T07:58Z
+last_built: 2024-09-19T02:58Z
 urls:
   reference: https://sydneybiox.github.io/wSIR/reference
   article: https://sydneybiox.github.io/wSIR/articles
diff --git a/reference/MouseData.html b/reference/MouseData.html
index 3c05a29..b0ef47c 100644
--- a/reference/MouseData.html
+++ b/reference/MouseData.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -93,7 +93,7 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/createWeightMatrix.html b/reference/createWeightMatrix.html
index e316d01..bc80e97 100644
--- a/reference/createWeightMatrix.html
+++ b/reference/createWeightMatrix.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -75,7 +75,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/exploreWSIRParams-1.png b/reference/exploreWSIRParams-1.png
index 84600b9..4470c10 100644
Binary files a/reference/exploreWSIRParams-1.png and b/reference/exploreWSIRParams-1.png differ
diff --git a/reference/exploreWSIRParams.html b/reference/exploreWSIRParams.html
index 6dd6e71..2b82782 100644
--- a/reference/exploreWSIRParams.html
+++ b/reference/exploreWSIRParams.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -52,16 +52,15 @@ <h1>exploreWSIRParams function</h1>
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">exploreWSIRParams</span><span class="op">(</span></span>
-<span>  <span class="va">exprs</span>,</span>
+<span>  <span class="va">X</span>,</span>
 <span>  <span class="va">coords</span>,</span>
 <span>  samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">coords</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>  alpha_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">10</span><span class="op">)</span>,</span>
 <span>  slice_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">10</span>, <span class="fl">15</span>, <span class="fl">20</span><span class="op">)</span>,</span>
-<span>  varThreshold <span class="op">=</span> <span class="fl">0.95</span>,</span>
-<span>  maxDirections <span class="op">=</span> <span class="fl">50</span>,</span>
 <span>  metric <span class="op">=</span> <span class="st">"DC"</span>,</span>
 <span>  nrep <span class="op">=</span> <span class="fl">5</span>,</span>
-<span>  param <span class="op">=</span> <span class="fu">MulticoreParam</span><span class="op">(</span>workers <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span>  param <span class="op">=</span> <span class="fu">MulticoreParam</span><span class="op">(</span>workers <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>,</span>
+<span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
@@ -69,7 +68,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
 
 
-<dl><dt id="arg-exprs">exprs<a class="anchor" aria-label="anchor" href="#arg-exprs"></a></dt>
+<dl><dt id="arg-x">X<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>matrix containing normalised gene expression data including n cells and p genes, dimension n * p.</p></dd>
 
 
@@ -96,17 +95,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 be no more than around \(\sqrt{n/20}\), as this upper bound ensures an average of at least 10 cells per tile in the training set.</p></dd>
 
 
-<dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric proportion of variance in <code>t(X_H) %*% W %*% X_H</code> to retain. Must be between 0 and 1. Default is 0.95.
-Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
-
-
-<dt id="arg-maxdirections">maxDirections<a class="anchor" aria-label="anchor" href="#arg-maxdirections"></a></dt>
-<dd><p>integer for the maximum number of directions to include in the low-dimensional embedding. Default is 50.</p></dd>
-
-
 <dt id="arg-metric">metric<a class="anchor" aria-label="anchor" href="#arg-metric"></a></dt>
-<dd><p>evaluation metric to use for parameter tuning to select optimal parameter combination. String, use "DC" to
+<dd><p>character single value. Evaluation metric to use for parameter tuning to select optimal parameter combination. String, use "DC" to
 use distance correlation, "CD" to use correlation of distances, or "ncol" for the number of dimensions in the low-dimensional
 embedding. Default is "DC".</p></dd>
 
@@ -119,6 +109,10 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>parallel computing setup for bplapply from BiocParallel package. Default is to use a single core, hence
 default value is MulticoreParam(workers = 1)</p></dd>
 
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arguments passed on to wSIROptimisation</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
@@ -138,26 +132,26 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">MouseData</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">explore_params</span> <span class="op">=</span> <span class="fu">exploreWSIRParams</span><span class="op">(</span>exprs <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
+<span class="r-in"><span><span class="va">explore_params</span> <span class="op">=</span> <span class="fu">exploreWSIRParams</span><span class="op">(</span>X <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
 <span class="r-in"><span>  coords <span class="op">=</span> <span class="va">sample1_coords</span>,</span></span>
 <span class="r-in"><span>  alpha_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">2</span>,<span class="fl">4</span>,<span class="fl">8</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  slice_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">6</span>,<span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.628118 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.9077346 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.16018 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.7087882 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.9227376 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.018222 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.8205791 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.7322271 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.9753475 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.6757081 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.7184703 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.802834 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.569175 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.8919282 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.118085 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.8686008 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.062988 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.002786 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.919801 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.8633406 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.124423 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.7145364 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.8456833 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 1.124386 secs</span>
 <span class="r-in"><span><span class="va">explore_params</span><span class="op">$</span><span class="va">plot</span></span></span>
 <span class="r-plt img"><img src="exploreWSIRParams-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="va">explore_params</span><span class="op">$</span><span class="va">message</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Optimal (alpha, slices) pair: (8, 3)"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Optimal (alpha, slices) pair: (0, 3)"</span>
 <span class="r-in"><span><span class="va">best_alpha</span> <span class="op">=</span> <span class="va">explore_params</span><span class="op">$</span><span class="va">best_alpha</span></span></span>
 <span class="r-in"><span><span class="va">best_slices</span> <span class="op">=</span> <span class="va">explore_params</span><span class="op">$</span><span class="va">best_slices</span></span></span>
 <span class="r-in"><span><span class="va">wsir_obj</span> <span class="op">=</span> <span class="fu"><a href="wSIR.html">wSIR</a></span><span class="op">(</span>X <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
@@ -177,7 +171,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/findTopGenes-1.png b/reference/findTopGenes-1.png
index 4d927b6..ceff5a1 100644
Binary files a/reference/findTopGenes-1.png and b/reference/findTopGenes-1.png differ
diff --git a/reference/findTopGenes.html b/reference/findTopGenes.html
index 898ff3c..67a1635 100644
--- a/reference/findTopGenes.html
+++ b/reference/findTopGenes.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -98,7 +98,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/generateUmapFromWSIR-1.png b/reference/generateUmapFromWSIR-1.png
index 86a6e20..1ec2802 100644
Binary files a/reference/generateUmapFromWSIR-1.png and b/reference/generateUmapFromWSIR-1.png differ
diff --git a/reference/generateUmapFromWSIR.html b/reference/generateUmapFromWSIR.html
index 6749278..d1dc339 100644
--- a/reference/generateUmapFromWSIR.html
+++ b/reference/generateUmapFromWSIR.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,7 +80,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">umap_coords</span> <span class="op">=</span> <span class="fu">generateUmapFromWSIR</span><span class="op">(</span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">top_genes_obj</span> <span class="op">=</span> <span class="fu"><a href="findTopGenes.html">findTopGenes</a></span><span class="op">(</span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span>, highest <span class="op">=</span> <span class="fl">4</span><span class="op">)</span> <span class="co"># create top genes object</span></span></span>
 <span class="r-in"><span><span class="va">umap_plot</span> <span class="op">=</span> <span class="fu"><a href="plotUmapFromWSIR.html">plotUmapFromWSIR</a></span><span class="op">(</span>umap_coords <span class="op">=</span> <span class="va">umap_coords</span>,</span></span>
-<span class="r-in"><span>  exprs <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
+<span class="r-in"><span>  X <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
 <span class="r-in"><span>  highest_genes <span class="op">=</span> <span class="va">top_genes_obj</span>,</span></span>
 <span class="r-in"><span>  n_genes <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">umap_plot</span></span></span>
@@ -97,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/index.html b/reference/index.html
index cf18b91..679a5ef 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -102,7 +102,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/plotUmapFromWSIR-1.png b/reference/plotUmapFromWSIR-1.png
index 86a6e20..1ec2802 100644
Binary files a/reference/plotUmapFromWSIR-1.png and b/reference/plotUmapFromWSIR-1.png differ
diff --git a/reference/plotUmapFromWSIR.html b/reference/plotUmapFromWSIR.html
index c9ea685..64b865a 100644
--- a/reference/plotUmapFromWSIR.html
+++ b/reference/plotUmapFromWSIR.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -46,7 +46,7 @@ <h1>plotUmapFromWSIR</h1>
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">plotUmapFromWSIR</span><span class="op">(</span></span>
-<span>  <span class="va">exprs</span>,</span>
+<span>  <span class="va">X</span>,</span>
 <span>  <span class="va">umap_coords</span>,</span>
 <span>  highest_genes <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  genes <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -59,7 +59,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
 
 
-<dl><dt id="arg-exprs">exprs<a class="anchor" aria-label="anchor" href="#arg-exprs"></a></dt>
+<dl><dt id="arg-x">X<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>matrix containing normalised gene expression data including n cells and p genes, dimension n * p.</p></dd>
 
 
@@ -67,7 +67,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>UMAP coordinates for each cell that is output of generateUmapFromWSIR function. The UMAP coordinates can be
 based on any dimension reduction method, e.g they could be the UMAP coordinates computed on the WSIR dimension reduction
 of the gene expression data, or on the PCs (principal components), or on any other low-dimensional matrix. Must be
-a matrix of dimension nrow(exprs) * 2.</p></dd>
+a matrix of dimension nrow(X) * 2.</p></dd>
 
 
 <dt id="arg-highest-genes">highest_genes<a class="anchor" aria-label="anchor" href="#arg-highest-genes"></a></dt>
@@ -76,7 +76,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt id="arg-genes">genes<a class="anchor" aria-label="anchor" href="#arg-genes"></a></dt>
-<dd><p>vector with gene names (must all be in colnames(exprs)) you wish to show in the UMAP plot. The cells
+<dd><p>vector with gene names (must all be in colnames(X)) you wish to show in the UMAP plot. The cells
 in those plots will be coloured by their expression values for the genes you provide here. Must provide either genes
 or highest_genes parameter (not both): provide genes if you want to visualise a few specific genes, provide highest_genes
 if you want to visualise the genes that are found to be the most important to the WSIR directions. Default is NULL
@@ -110,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">umap_coords</span> <span class="op">=</span> <span class="fu"><a href="generateUmapFromWSIR.html">generateUmapFromWSIR</a></span><span class="op">(</span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">top_genes_obj</span> <span class="op">=</span> <span class="fu"><a href="findTopGenes.html">findTopGenes</a></span><span class="op">(</span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span>, highest <span class="op">=</span> <span class="fl">4</span><span class="op">)</span> <span class="co"># create top genes object</span></span></span>
 <span class="r-in"><span><span class="va">umap_plot</span> <span class="op">=</span> <span class="fu">plotUmapFromWSIR</span><span class="op">(</span>umap_coords <span class="op">=</span> <span class="va">umap_coords</span>,</span></span>
-<span class="r-in"><span>  exprs <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
+<span class="r-in"><span>  X <span class="op">=</span> <span class="va">sample1_exprs</span>,</span></span>
 <span class="r-in"><span>  highest_genes <span class="op">=</span> <span class="va">top_genes_obj</span>,</span></span>
 <span class="r-in"><span>  n_genes <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">umap_plot</span></span></span>
@@ -127,7 +127,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/projectWSIR.html b/reference/projectWSIR.html
index 63f0535..4a89f84 100644
--- a/reference/projectWSIR.html
+++ b/reference/projectWSIR.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -85,7 +85,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/sirCategorical.html b/reference/sirCategorical.html
index 4bdcf28..3e5ba18 100644
--- a/reference/sirCategorical.html
+++ b/reference/sirCategorical.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -42,7 +42,7 @@ <h1>sirCategorical</h1>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sirCategorical</span><span class="op">(</span><span class="va">X</span>, <span class="va">Y</span>, directions <span class="op">=</span> <span class="fl">50</span>, W <span class="op">=</span> <span class="cn">NULL</span>, varThreshold <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sirCategorical</span><span class="op">(</span><span class="va">X</span>, <span class="va">Y</span>, maxDirections <span class="op">=</span> <span class="fl">50</span>, W <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
@@ -58,7 +58,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 be up to slices^2 unique tile IDs in this column.</p></dd>
 
 
-<dt id="arg-directions">directions<a class="anchor" aria-label="anchor" href="#arg-directions"></a></dt>
+<dt id="arg-maxdirections">maxDirections<a class="anchor" aria-label="anchor" href="#arg-maxdirections"></a></dt>
 <dd><p>Integer to specify maximum number of directions to retain in thee low-dimensional embedding
 of the data. Use if you need at most a certain number for a downstream task.</p></dd>
 
@@ -67,11 +67,6 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>Weight matrix created by createWeightMatrix. Entry (i,j) represents the spatial correlation level
 between tiles i and j. The diagonal values should be all 1. If not provided, SIR implementation will be used.</p></dd>
 
-
-<dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric value specifying the desired proportion of variance to retain. If e.g 95% (default),
-then number of directions used will be such that their eigenvalues add up to 95% of the sum of all eigenvalues.</p></dd>
-
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
@@ -88,7 +83,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/sirPCA.html b/reference/sirPCA.html
index 259cb9c..b5628d9 100644
--- a/reference/sirPCA.html
+++ b/reference/sirPCA.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>sirPCA — sirPCA • wSIR</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="sirPCA — sirPCA"><meta name="description" content="This function performs eigendecomposition on (X^H)^t %% W %% (X^H), where X^H is matrix of scaled slice means."><meta property="og:description" content="This function performs eigendecomposition on (X^H)^t %% W %% (X^H), where X^H is matrix of scaled slice means."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>sirPCA — sirPCA • wSIR</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="sirPCA — sirPCA"><meta name="description" content="This function performs eigendecomposition on (X^H)^t %*% W %*% (X^H), where X^H is matrix of scaled slice means."><meta property="og:description" content="This function performs eigendecomposition on (X^H)^t %*% W %*% (X^H), where X^H is matrix of scaled slice means."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,16 +37,16 @@ <h1>sirPCA</h1>
     </div>
 
     <div class="ref-description section level2">
-    <p>This function performs eigendecomposition on (X^H)^t %<em>% W %</em>% (X^H), where X^H is matrix of scaled slice means.</p>
+    <p>This function performs eigendecomposition on <code>(X^H)^t %*% W %*% (X^H)</code>, where <code>X^H</code> is matrix of scaled slice means.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sirPCA</span><span class="op">(</span></span>
 <span>  <span class="va">sliced_data</span>,</span>
-<span>  <span class="va">directions</span>,</span>
+<span>  maxDirections <span class="op">=</span> <span class="fl">50</span>,</span>
 <span>  W <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/diag.html" class="external-link">diag</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">sliced_data</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  varThreshold <span class="op">=</span> <span class="fl">0.95</span></span>
+<span>  varThreshold <span class="op">=</span> <span class="fl">0.99</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
@@ -58,8 +58,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>matrix of scaled slice means</p></dd>
 
 
-<dt id="arg-directions">directions<a class="anchor" aria-label="anchor" href="#arg-directions"></a></dt>
-<dd><p>integer, number of directions we want in our final low-dimensional Z.</p></dd>
+<dt id="arg-maxdirections">maxDirections<a class="anchor" aria-label="anchor" href="#arg-maxdirections"></a></dt>
+<dd><p>integer (default 50), number of directions we want in our final low-dimensional Z.</p></dd>
 
 
 <dt id="arg-w">W<a class="anchor" aria-label="anchor" href="#arg-w"></a></dt>
@@ -67,15 +67,15 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric proportion of eigenvalues of variance in t(X_H) %<em>% W %</em>% X_H to retain.
-Default is 0.95. Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
+<dd><p>numeric proportion of eigenvalues of variance in <code>t(X_H) %*% W %*% X_H</code> to retain.
+Default is 0.99. Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    <p>list containing: 1) "eigenvectors" matrix of eigenvectors of (X^H)^t %<em>% W %</em>% (X^H)
+    <p>list containing: 1) "eigenvectors" matrix of eigenvectors of <code>(X^H)^t %*% W %*% (X^H)</code>
 2) "d" integer, selected number of dimensions
-3) "evalues" vector of eigenvalues of (X^H)^t %<em>% W %</em>% (X^H)</p>
+3) "evalues" vector of eigenvalues of <code>(X^H)^t %*% W %*% (X^H)</code></p>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
@@ -87,7 +87,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/slicerCategorical.html b/reference/slicerCategorical.html
index 403ae43..c272d64 100644
--- a/reference/slicerCategorical.html
+++ b/reference/slicerCategorical.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -79,7 +79,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/spatialAllocator.html b/reference/spatialAllocator.html
index cdbbe82..46dfd7a 100644
--- a/reference/spatialAllocator.html
+++ b/reference/spatialAllocator.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -75,7 +75,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/visualiseWSIRDirections-1.png b/reference/visualiseWSIRDirections-1.png
index 57185e2..15f3e35 100644
Binary files a/reference/visualiseWSIRDirections-1.png and b/reference/visualiseWSIRDirections-1.png differ
diff --git a/reference/visualiseWSIRDirections.html b/reference/visualiseWSIRDirections.html
index 38f9260..b048d9c 100644
--- a/reference/visualiseWSIRDirections.html
+++ b/reference/visualiseWSIRDirections.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -98,7 +98,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  optim_params <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
 <span class="r-in"><span>  alpha <span class="op">=</span> <span class="fl">4</span>,</span></span>
 <span class="r-in"><span>  slices <span class="op">=</span> <span class="fl">6</span><span class="op">)</span> <span class="co"># create wsir object</span></span></span>
-<span class="r-in"><span><span class="va">vis_obj</span> <span class="op">=</span> <span class="fu">visualiseWSIRDirections</span><span class="op">(</span>coords <span class="op">=</span> <span class="va">sample1_coords</span>, WSIR <span class="op">=</span> <span class="va">wsir_obj</span>, dirs <span class="op">=</span> <span class="fl">8</span><span class="op">)</span> <span class="co"># create visualisations</span></span></span>
+<span class="r-in"><span><span class="va">vis_obj</span> <span class="op">=</span> <span class="fu">visualiseWSIRDirections</span><span class="op">(</span>coords <span class="op">=</span> <span class="va">sample1_coords</span>,</span></span>
+<span class="r-in"><span>WSIR <span class="op">=</span> <span class="va">wsir_obj</span>, dirs <span class="op">=</span> <span class="fl">8</span><span class="op">)</span> <span class="co"># create visualisations</span></span></span>
 <span class="r-in"><span><span class="va">vis_obj</span></span></span>
 <span class="r-plt img"><img src="visualiseWSIRDirections-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
@@ -113,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/wSIR.html b/reference/wSIR.html
index 9e75c73..0c3a465 100644
--- a/reference/wSIR.html
+++ b/reference/wSIR.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -51,16 +51,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">wSIR</span><span class="op">(</span></span>
 <span>  <span class="va">X</span>,</span>
 <span>  <span class="va">coords</span>,</span>
-<span>  samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">coords</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  slices <span class="op">=</span> <span class="fl">8</span>,</span>
-<span>  alpha <span class="op">=</span> <span class="fl">4</span>,</span>
-<span>  maxDirections <span class="op">=</span> <span class="fl">50</span>,</span>
-<span>  varThreshold <span class="op">=</span> <span class="fl">0.95</span>,</span>
-<span>  optim_params <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  optim_params <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  alpha_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">8</span>, <span class="fl">12</span><span class="op">)</span>,</span>
 <span>  slice_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">15</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>  metric <span class="op">=</span> <span class="st">"DC"</span>,</span>
-<span>  nrep <span class="op">=</span> <span class="fl">5</span></span>
+<span>  nrep <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
@@ -76,50 +73,22 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>dataframe containing spatial positions of n cells in 2D space. Dimension n * 2. Column names must be c("x", "y").</p></dd>
 
 
-<dt id="arg-samples">samples<a class="anchor" aria-label="anchor" href="#arg-samples"></a></dt>
-<dd><p>sample ID of each cell. In total, must have length equal to the number of cells. For example, if
-your dataset has 10000 cells, the first 5000 from sample 1 and the remaining 5000 from sample 2, you would write
-samples = c(rep(1, 5000), rep(2, 5000)) to specify that the first 5000 cells are sample 1 and the remaining are sample 2.
-Default is that all cells are from sample 1. Sample IDs can be of any format: for the previous example, you could write
-samples = c(rep("sample 1", 5000), rep("sample 2", 5000)), and the result would be the same.</p></dd>
-
-
-<dt id="arg-slices">slices<a class="anchor" aria-label="anchor" href="#arg-slices"></a></dt>
-<dd><p>integer for number of slices on each axis of tissue. For example, slices = 4 creates 4 slices along
-each spatial axis, yielding 4^2 = 16 tiles. Default is 8, suggested minimum of 3. Suggest to tune this parameter.</p></dd>
-
-
-<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
-<dd><p>integer to specify desired strength of spatial correlation. Suggest to tune this parameter on testing
-dataset among e.g values c(0,2,4,8). alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations.</p></dd>
-
-
-<dt id="arg-maxdirections">maxDirections<a class="anchor" aria-label="anchor" href="#arg-maxdirections"></a></dt>
-<dd><p>integer for upper limit on number of directions to include in low-dimensional space. Use if you
-need less than a certain number of dimensions for downstream analyes</p></dd>
-
-
-<dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric proportion of eigenvalues of variance in t(X_H) %<em>% W %</em>% X_H to retain. Default is 0.95.
-Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
-
-
 <dt id="arg-optim-params">optim_params<a class="anchor" aria-label="anchor" href="#arg-optim-params"></a></dt>
-<dd><p>logical, if you would like wSIR to automatically optimise parameters slices and alpha based on
-either distance correlation or correlation of distances as evaluation metric. Default is TRUE. If your downstream
+<dd><p>logical default FALSE. If you would like wSIR to automatically optimise parameters slices and alpha based on
+either distance correlation or correlation of distances as evaluation metric. If your downstream
 task is quite different to either of those metrics, then we suggest you tune those two parameters yourself using
 your specific task and evaluation metric. In that case, determine your optimal slices and alpha values and then use
 them in the relevant function, then setting optim_params = FALSE.</p></dd>
 
 
 <dt id="arg-alpha-vals">alpha_vals<a class="anchor" aria-label="anchor" href="#arg-alpha-vals"></a></dt>
-<dd><p>If you have optim_params = TRUE, then this is the values of alpha to optimise over in WSIR. 0
+<dd><p>If you have optim_params = TRUE, then this is the values of alpha to optimise over in wSIR. 0
 gives Sliced Inverse Regression (SIR) implementation, and larger values represent stronger spatial correlation.
 Suggest to use integers for interpretability, but can use non-integers. Values must be non-negative.</p></dd>
 
 
 <dt id="arg-slice-vals">slice_vals<a class="anchor" aria-label="anchor" href="#arg-slice-vals"></a></dt>
-<dd><p>If you have optim_params = TRUE, then this is the values of slices to optimise over in WSIR.
+<dd><p>If you have optim_params = TRUE, then this is the values of slices to optimise over in wSIR.
 Suggest maximum value in the vector to be no more than around \(\sqrt{n/20}\), as this upper bound ensures an
 average of at least 10 cells per tile in the training set.</p></dd>
 
@@ -133,16 +102,24 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>If optim_params = TRUE, this is the integer for the number of train/test splits of the data to
 perform during optimisation of parameters slices and alpha.</p></dd>
 
+
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
+<dd><p>logical (default FALSE) whether progress messages should be printed</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arguments passing to <code>exploreWSIRParams</code> and <code>wSIRSpecifiedParams</code></p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>wSIR object which is a list containing 5 (named) positions. 1) scores matrix containing low-dimensional
-representation of X from wSIR algorithm. Dimension n * d, where d is chosen to include at least varThreshold proportion of variance.
-2) directions matrix containing WSIR directions, dimension p * d. Use this to project new data into low-dimensional space via X_new %<em>% directions.
+representation of X from wSIR algorithm. Dimension <code>n * d</code>, where d is chosen to include at least varThreshold proportion of variance.
+2) directions matrix containing wSIR directions, dimension <code>p * d</code>. Use this to project new data into low-dimensional space via X_new %*% directions.
 3) estd integer stating how many dimensions in the computed low-dimensional space are needed to account for varThreshold
 proportion of variance. Same as number of dimensions in scores.
 4) W matrix weight matrix from cells_weight_matrix2 function
-5) evalues vector containing p eigenvalues of t(X_H) %</em>% W %*% X_H. varThreshold parameter works on these evalues,
+5) evalues vector containing p eigenvalues of <code>t(X_H) %*% W %*% X_H</code>. varThreshold parameter works on these evalues,
 such that e.g the first j directions are included if the sum of the first j evalues equals 0.95% of the sum of all evalues.</p>
     </div>
 
@@ -154,17 +131,17 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  optim_params <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
 <span class="r-in"><span>  alpha_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">2</span>,<span class="fl">4</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  slice_vals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">6</span>,<span class="fl">10</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  metric <span class="op">=</span> <span class="st">"CD"</span>,</span></span>
+<span class="r-in"><span>  metric <span class="op">=</span> <span class="st">"DC"</span>,</span></span>
 <span class="r-in"><span>  nrep <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="co"># create wsir object</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.134021 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1995811 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.2074399 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1596627 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1758702 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1792307 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1397426 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1735837 secs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1792359 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1707692 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.3771851 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1902878 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1471441 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1736453 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1933596 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1468866 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.3150549 secs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Time difference of 0.1909339 secs</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -177,7 +154,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/wSIROptimisation.html b/reference/wSIROptimisation.html
index e182e10..6b86506 100644
--- a/reference/wSIROptimisation.html
+++ b/reference/wSIROptimisation.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -51,8 +51,8 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">slices</span>,</span>
 <span>  <span class="va">alpha</span>,</span>
 <span>  <span class="va">maxDirections</span>,</span>
-<span>  <span class="va">varThreshold</span>,</span>
-<span>  metrics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD"</span>, <span class="st">"DC"</span>, <span class="st">"ncol"</span><span class="op">)</span></span>
+<span>  evalmetrics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD"</span>, <span class="st">"DC"</span>, <span class="st">"ncol"</span><span class="op">)</span>,</span>
+<span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
@@ -95,16 +95,15 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>integer for the maximum number of directions to include in the low-dimenensional embedding. Default is 50.</p></dd>
 
 
-<dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric proportion of variance in <code>t(X_H) %*% W %*% X_H</code> to retain. Must be between 0 and 1. Default is 0.95.
-Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
-
-
-<dt id="arg-metrics">metrics<a class="anchor" aria-label="anchor" href="#arg-metrics"></a></dt>
+<dt id="arg-evalmetrics">evalmetrics<a class="anchor" aria-label="anchor" href="#arg-evalmetrics"></a></dt>
 <dd><p>evaluation metrics to use for parameter tuning. String, options are any or all of: "DC" to use distance
 correlation; "CD" to use correlation of distances; "ncol" to use number of columns in low-dimensional embedding. Default is all three,
 specified by metrics = c("DC", "CD", "ncol").</p></dd>
 
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arguments passed to wSIRSpecifiedParams</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
@@ -120,7 +119,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/wSIRSpecifiedParams.html b/reference/wSIRSpecifiedParams.html
index 13e78ba..690b4c8 100644
--- a/reference/wSIRSpecifiedParams.html
+++ b/reference/wSIRSpecifiedParams.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">wSIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -48,11 +48,11 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">wSIRSpecifiedParams</span><span class="op">(</span></span>
 <span>  <span class="va">X</span>,</span>
 <span>  <span class="va">coords</span>,</span>
+<span>  group <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">coords</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>  slices <span class="op">=</span> <span class="fl">8</span>,</span>
 <span>  alpha <span class="op">=</span> <span class="fl">4</span>,</span>
-<span>  maxDirections <span class="op">=</span> <span class="fl">50</span>,</span>
-<span>  varThreshold <span class="op">=</span> <span class="fl">0.95</span></span>
+<span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
@@ -68,6 +68,10 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>dataframe containing spatial positions of n cells in 2D space. Dimension n * 2. Column names must be c("x", "y").</p></dd>
 
 
+<dt id="arg-group">group<a class="anchor" aria-label="anchor" href="#arg-group"></a></dt>
+<dd><p>a factor indicating group level information for cells within and across samples (e.g. cell type).</p></dd>
+
+
 <dt id="arg-samples">samples<a class="anchor" aria-label="anchor" href="#arg-samples"></a></dt>
 <dd><p>sample ID of each cell. In total, must have length equal to the number of cells. For example, if
 your dataset has 10000 cells, the first 5000 from sample 1 and the remaining 5000 from sample 2, you would write
@@ -86,14 +90,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 dataset among e.g values c(0,2,4,8). alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations.</p></dd>
 
 
-<dt id="arg-maxdirections">maxDirections<a class="anchor" aria-label="anchor" href="#arg-maxdirections"></a></dt>
-<dd><p>integer for upper limit on number of directions to include in low-dimensional space. Use if you
-need less than a certain number of dimensions for downstream analyes</p></dd>
-
-
-<dt id="arg-varthreshold">varThreshold<a class="anchor" aria-label="anchor" href="#arg-varthreshold"></a></dt>
-<dd><p>numeric proportion of eigenvalues of variance in t(X_H) %<em>% W %</em>% X_H to retain. Default is 0.95.
-Select higher threshold to include more dimensions, lower threshold to include less dimensions.</p></dd>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arguments passed to sirCategorical</p></dd>
 
 </dl></div>
     <div class="section level2">
@@ -117,7 +115,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/search.json b/search.json
index e9f20e2..c8d3055 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Weighted Sliced Inverse Regression (WSIR)","text":"Weighted Sliced Inverse Regression (wSIR) supervised dimension reduction technique spatial transcriptomics data. method uses cell’s gene expression values covariates spatial position response. allows us create low-dimensional representation gene expression data retains information spatial patterns present gene expression data. resulting mapping gene expression data spatially aware low-dimensional embedding can used project new single-cell gene expression data low-dimensional space preserves ability predict cell’s spatial location low-dimensional embedding.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"method-overview","dir":"Articles","previous_headings":"Introduction","what":"Method Overview","title":"Weighted Sliced Inverse Regression (WSIR)","text":"wSIR extension supervised dimension reduction technique Sliced Inverse Regression (SIR). SIR existing supervised dimension reduction method works grouping together observations similar values response. spatial transcriptomics data, means grouping cells certain number tiles based spatial position. example, use 4 tiles, cells top right quadrant image/tissue go one group, top left another, . groups summarised averaging expression level cells group genes. , eigendecomposition performed resulting matrix tile-gene means, returning SIR directions SIR scores. motivation behind wSIR SIR uses cell’s spatial position separating cells given number groups/tiles. groups created, lose fact groups may spatially related (come adjacent tiles) groups (come opposite sides tissue). wSIR uses weight matrix incorporate spatial correlation pairs cells SIR algorithm. matrix dimension H*H, H number tiles, (,j)th entry represents distance tiles j. matrix incorporated eigendeomposition step. wSIR output structure SIR output.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"vignette-overview","dir":"Articles","previous_headings":"Introduction","what":"Vignette Overview","title":"Weighted Sliced Inverse Regression (WSIR)","text":"vignette, demonstrate use WSIR obtain low-dimensional embedding gene expression data. explore embedding using package’s built-functions. However, low-dimensional matrix importantly used downstream tasks benefit lower-dimensional representation gene expression data preserves information cell’s spatial location. perform basic downstream analysis demonstrate practicality wSIR.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Weighted Sliced Inverse Regression (WSIR)","text":"use data Lohoff et al, 2021. code allow download mouse data using MouseGastrulationData scran R packages. randomly sample 20% cells three biological replicate samples. vignette examples function wSIR, simply load data already saved data/MouseData.rda.","code":"# Packages needed to download data #library(scran) # for logNormCounts #library(MouseGastrulationData) # to download the data for this vignette  set.seed(2024)  seqfish_data_sample1 <- LohoffSeqFISHData(samples = c(1,2)) seqfish_data_sample1 = logNormCounts(seqfish_data_sample1) # log transform variance stabilising rownames(seqfish_data_sample1) <- rowData(seqfish_data_sample1)[,\"SYMBOL\"] # change rownames to gene symbols that are consistent sample1_exprs = t(assay(seqfish_data_sample1, \"logcounts\")) # extract matrix of gene expressions sample1_coords = spatialCoords(seqfish_data_sample1)[,1:2] %>% as.data.frame() colnames(sample1_coords) = c(\"x\", \"y\")  seqfish_data_sample2 <- LohoffSeqFISHData(samples = c(3,4)) seqfish_data_sample2 = logNormCounts(seqfish_data_sample2) rownames(seqfish_data_sample2) <- rowData(seqfish_data_sample2)[,\"SYMBOL\"] sample2_exprs = t(assay(seqfish_data_sample2, \"logcounts\")) sample2_coords = spatialCoords(seqfish_data_sample2)[,1:2] %>% as.data.frame() colnames(sample2_coords) = c(\"x\", \"y\")  seqfish_data_sample3 <- LohoffSeqFISHData(samples = c(5,6)) seqfish_data_sample3 = logNormCounts(seqfish_data_sample3) rownames(seqfish_data_sample3) <- rowData(seqfish_data_sample3)[,\"SYMBOL\"] sample3_exprs = t(assay(seqfish_data_sample3, \"logcounts\")) sample3_coords = spatialCoords(seqfish_data_sample3)[,1:2] %>% as.data.frame() colnames(sample3_coords) = c(\"x\", \"y\")  keep1 = sample(c(TRUE, FALSE), nrow(sample1_exprs), replace = TRUE, prob = c(0.2, 0.8)) keep2 = sample(c(TRUE, FALSE), nrow(sample2_exprs), replace = TRUE, prob = c(0.2, 0.8)) keep3 = sample(c(TRUE, FALSE), nrow(sample3_exprs), replace = TRUE, prob = c(0.2, 0.8))  sample1_exprs = sample1_exprs[keep1,] sample1_coords = sample1_coords[keep1,] sample2_exprs = sample2_exprs[keep2,] sample2_coords = sample2_coords[keep2,] sample3_exprs = sample3_exprs[keep3,] sample3_coords = sample3_coords[keep3,]  sample1_cell_types = seqfish_data_sample1$celltype[keep1] sample2_cell_types = seqfish_data_sample2$celltype[keep2] sample3_cell_types = seqfish_data_sample3$celltype[keep3]  save(sample1_exprs, sample1_coords, sample1_cell_types,       sample2_exprs, sample2_coords, sample2_cell_types,       sample3_exprs, sample3_coords, sample3_cell_types,       file = \"../data/MouseData.rda\", compress = \"xz\") data(MouseData)"},{"path":[]},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"parameter-study","dir":"Articles","previous_headings":"Supervised dimension reduction with wSIR","what":"Parameter Study","title":"Weighted Sliced Inverse Regression (WSIR)","text":"wSIR contains two parameters, alpha slices. Parameter slices number groups along spatial axis split observations wSIR algorithm. slices means pick spatial information gene expression data, risk overfitting training set. Parameter alpha modifies strength weight matrix. alpha = 0 equivalent spatial weighting, meaning weight matrix becomes identity matrix. equivalent SIR. Larger alpha values means weight given spatial correlation rather gene expression differences alone computation wSIR directions. function exploreWSIRParams performs wSIR many choices alpha slices identify appropriate parameters moving forward. , use sample 1 . parameters tuned reasonable values. function exploreWSIRParams computes visualises performance wSIR given combinations slices alpha. performance computed following procedure: 1) combination slices alpha, split data 50% train 50% test (train test halves include gene expression data cell coordinates). 2) Perform wSIR training set using current combination slices alpha. 3) Project gene expression data testing set low-dimensional space using wSIR results step 2. 4) Evaluate wSIR’s performance computing either distance correlation (“DC”, default), correlation distances (“CD”) projected gene expression data test set test coordinates, according parameter metric. 5) Repeat steps 2-4 done nrep times (nrep parameter whose default 5). Calculate average metric value nrep iterations combination slices alpha. 6) Repeat steps 1-5 combinations slices alpha obtain average metric value combination. 7) Return combination slices alpha highest average metric value display plot showing performance every combination parameters. Note: key advantage wSIR SIR parameter robustness. , can see SIR’sperformance deteriorates use larger values slices. However, wSIR’s performance relatively stable vary slices alpha across reasonable values (e.g. among default values optimise ). following plot, metric value becomes smaller increase number slices α=0\\alpha = 0 (corresponds SIR). Performance stable non-zero alpha slice combinations (correspond wSIR).","code":"a <- Sys.time() optim_obj = exploreWSIRParams(exprs = sample1_exprs,                                coords = sample1_coords,                                alpha_vals = c(0,.5, 1,2,4,8),                                slice_vals = c(5,10,15),                               nrep = 20, metric = \"CD\") ## Time difference of 3.836403 secs ## Time difference of 4.253446 secs ## Time difference of 4.422207 secs ## Time difference of 3.272594 secs ## Time difference of 4.135383 secs ## Time difference of 4.446194 secs ## Time difference of 3.394855 secs ## Time difference of 4.115742 secs ## Time difference of 4.265517 secs ## Time difference of 2.989446 secs ## Time difference of 3.536304 secs ## Time difference of 3.744084 secs ## Time difference of 3.000982 secs ## Time difference of 3.266891 secs ## Time difference of 3.711689 secs ## Time difference of 3.043132 secs ## Time difference of 3.222942 secs ## Time difference of 3.594974 secs Sys.time()-a ## Time difference of 1.1131 mins optim_obj$plot"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-computation","dir":"Articles","previous_headings":"Supervised dimension reduction with wSIR","what":"wSIR Computation","title":"Weighted Sliced Inverse Regression (WSIR)","text":"next perform wSIR using optimal parameter combination found previous section. use gene expression matrix spatial coordinates sample 1 . returns list results 5 (named) slots, whose details can found ?wSIR::wSIR.","code":"wsir_obj = wSIR(X = sample1_exprs,                  coords = sample1_coords,                  slices = optim_obj$best_slices,                  alpha = optim_obj$best_alpha,                 optim_params = FALSE)  names(wsir_obj) ## [1] \"scores\"     \"directions\" \"estd\"       \"W\"          \"evalues\""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-results-analysis","dir":"Articles","previous_headings":"","what":"wSIR Results Analysis","title":"Weighted Sliced Inverse Regression (WSIR)","text":"section, demonstrate use built-analysis functions better understand wSIR creates spatially-informed low-dimensional embedding. functions use wSIR result input. , use output previous section, meaning studying result performing wSIR sample 1 .","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-top-genes","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"wSIR Top Genes","title":"Weighted Sliced Inverse Regression (WSIR)","text":"findTopGenes function finds plots genes highest loading specified wSIR directions (default direction 1). gene high loading (terms magnitude), important wSIR direction. Since wSIR directions designed retain information cell’s spatial position, genes high loading spatially-related genes. plot , can see genes highest loading wSIR direction 1. useful gives us intuition wSIR creates low-dimensional embedding. can see genes known spatial genes (e.g. Cdx2, Hox-), expect see. can simultaneously plot top genes multiple directions, utilise ","code":"top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8) # create top genes object top_genes_plot = top_genes_obj$plot # select plot top_genes_plot # print plot top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8, dirs = 2:4) top_genes_plot = top_genes_obj$plot top_genes_plot"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"visualising-wsir-scores","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"Visualising wSIR Scores","title":"Weighted Sliced Inverse Regression (WSIR)","text":"visualiseWSIRDirections function plots cell spatial position, coloured value specified wSIR columns. gives us understanding column low-dimensional embedding represents. , visualise cells spatial positions, coloured 5 wSIR directions top left plot illustrates , example, wSIR direction 1 captures information “y” spatial axis, since cells higher “y” coordinate low wSIR1 value, cells lower “y” coordinate higher wSIR1 value. wSIR2 shown next plot (one titled “2”), can see wSIR column two appears capture information “x” spatial coordinate. remaining three wSIR columns contain information cell types, can tell regions high low wSIR column values spread across tissue.","code":"vis_obj = visualiseWSIRDirections(coords = sample1_coords, WSIR = wsir_obj, dirs = 8) # create visualisations vis_obj"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"umap-on-low-dimensional-embedding","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"UMAP on low-dimensional embedding","title":"Weighted Sliced Inverse Regression (WSIR)","text":"two functions generateUmapFromWSIR plotUmapFromWSIR create display UMAP dimension reduction calculated wSIR low-dimensional embedding. can colour UMAP plot (point represents cell) value various genes interest. visualises structure wSIR dimension reduction space, useful gain intuition space represents. Specifically, can see wSIR space contains neighbourhoods high expression specific genes, thus better understanding space made. specify genes like include, can use output findTopGenes function , finds spatially-related genes ranking highest loading relevant wSIR directions. output value highest_genes parameter. Otherwise, also specify genes interest specific genes like visualise. example, wanted visualise expression distribution Cdx2 Hoxb4, use genes = c(\"Cdx2\", \"Hoxb4\") argument plotUmapFromWSIR (leave highest_genes blank). , use UMAP function visualise wSIR space computed gene expression data sample 1. colour cell values 6 genes highest value wSIR direction 1 (found findTopGenes function previously). can see genes, specific regions high expression UMAP plots, suggesting wSIR space separates cells based expression genes.","code":"umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) umap_plots = plotUmapFromWSIR(exprs = sample1_exprs,                               umap_coords = umap_coords,                               highest_genes = top_genes_obj,                               n_genes = 6) umap_plots"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"projection-of-new-data-with-wsir","dir":"Articles","previous_headings":"","what":"Projection of new data with wSIR","title":"Weighted Sliced Inverse Regression (WSIR)","text":"key functionality wSIR package ability project new single-cell data wSIR low-dimensional space. allow low-dimensional representation gene expression data contains information cell’s spatial position even though access spatial coordinates new data. low-dimensional wSIR embedding especially useful downstream applications, like spatial alignment spatial clustering (don’t spatial coordinates). , demonstrate steps , well specific application. projection example, perform wSIR spatial transcriptomics dataset includes gene expression data spatial coordinates. project “single-cell” dataset, contains gene expression matrix, wSIR low-dimensional space.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"single-sample-spatial-dataset-single-sample-single-cell-dataset","dir":"Articles","previous_headings":"Projection of new data with wSIR","what":"Single-sample spatial dataset, single-sample single-cell dataset","title":"Weighted Sliced Inverse Regression (WSIR)","text":", demonstrate steps project new sample single-cell dataset wSIR low-dimensional space already performed wSIR spatial transcriptomics dataset first sample. already performed wSIR sample 1, project sample 2’s gene expression matrix wSIR low-dimensional space, therefore ability predict sample 2’s (unknown stage) spatial locations. Check dimension sample 2’s low-dimensional gene expression data: Observe sample 2’s low-dimensional gene expression data: low-dimensional gene expression data can used later tasks benefit low-dimensional embedding gene expression data samples, rather just gene expression data.","code":"sample2_low_dim_exprs = projectWSIR(wsir = wsir_obj, newdata = sample2_exprs) dim(sample2_low_dim_exprs) ## [1] 2986    9 head(sample2_low_dim_exprs) ##           [,1]       [,2]        [,3]         [,4]       [,5]        [,6] ## [1,] 1.2044363 -0.3790739 -1.72818610 -1.797653672 -0.8684785 -0.74469382 ## [2,] 0.5738578  0.4719606  0.26341538 -0.341280096 -0.4274574  0.34500227 ## [3,] 1.2749872 -0.8741444 -1.34429322  0.009172746  0.4562988  0.42206043 ## [4,] 1.7326508 -0.3322124 -0.55148621  0.732668048 -0.2272738  0.30675261 ## [5,] 2.1501634 -0.8665235  0.04544417  0.779635209 -0.4277316  0.17938548 ## [6,] 1.2074795 -1.1035173  0.51632770  1.048309253 -1.0852001 -0.01835717 ##          [,7]      [,8]       [,9] ## [1,] 3.437305 0.5718475 -0.4848100 ## [2,] 1.304516 1.7813543  0.6194540 ## [3,] 2.313171 0.0540894 -0.3177639 ## [4,] 2.269962 0.5799911 -1.2385839 ## [5,] 0.899609 1.5397581  0.1789930 ## [6,] 1.244870 0.5720243  0.4845666"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"multi-sample-spatial-dataset-single-sample-single-cell-dataset","dir":"Articles","previous_headings":"Projection of new data with wSIR","what":"Multi-sample spatial dataset, single-sample single-cell dataset","title":"Weighted Sliced Inverse Regression (WSIR)","text":", perform wSIR samples 1 2 together. requires gene expression matrices samples joined together one matrix, coords dataframes joined one dataframe. concatenation using rbind. specify sample IDs row joined expression matrix coordinates dataframe using samples argument. vector “1” row sample 1 “2” row sample 2. use resulting wSIR output project gene expression data sample 3 low-dimensional space. check dimension resulting matrix. low-dimensional matrix can used downstream tasks benefit low-dimensional embedding sample 3’s gene expression matrix contains information cell’s location. Examples downstream use include spatial alignment single-cell spatial gene expression data via Tangram, using wSIR scores input rather (unreduced) gene expression matrix. example simple application using wSIR scores input KNN cell type classification algorithm. demonstrated , using ‘knn’ function ‘class’ package. code, use wSIR scores input simple KNN-based cell type classification tool. print tail prediction vector, demonstrating can use wSIR-based low-dimensional gene expression data new sample step realistic analysis pipeline.","code":"wsir_obj_samples12 <- wSIR(X = rbind(sample1_exprs, sample2_exprs),                            coords = rbind(sample1_coords, sample2_coords),                            samples = c(rep(1, nrow(sample1_coords)), rep(2, nrow(sample2_coords))),                            slices = optim_obj$best_slices,                             alpha = optim_obj$best_alpha,                            optim_params = FALSE)  sample3_low_dim_exprs <- projectWSIR(wsir = wsir_obj_samples12, newdata = sample3_exprs) dim(sample3_low_dim_exprs) ## [1] 4607    7 samples12_cell_types = append(sample1_cell_types, sample2_cell_types)  knn_classification_object = knn(train = wsir_obj_samples12$scores,                                  test = sample3_low_dim_exprs,                                 cl = samples12_cell_types,                                 k = 10)  tail(knn_classification_object) ## [1] Presomitic mesoderm Dermomyotome        Presomitic mesoderm ## [4] Spinal cord         Spinal cord         Low quality         ## 24 Levels: Allantois Anterior somitic tissues ... Surface ectoderm sessionInfo() ## R version 4.4.1 (2024-06-14) ## Platform: x86_64-pc-linux-gnu ## Running under: Ubuntu 22.04.4 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        ##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    ##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           ## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] class_7.3-22       umap_0.2.10.0      vctrs_0.6.5        doBy_4.6.22        ##  [5] Rfast_2.1.0        RcppParallel_5.1.9 RcppZiggurat_0.1.6 Rcpp_1.0.13        ##  [9] ggplot2_3.5.1      magrittr_2.0.3     wSIR_0.1.2         BiocStyle_2.32.1   ##  ## loaded via a namespace (and not attached): ##  [1] gtable_0.3.5         xfun_0.47            bslib_0.8.0          ##  [4] lattice_0.22-6       tools_4.4.1          generics_0.1.3       ##  [7] parallel_4.4.1       tibble_3.2.1         fansi_1.0.6          ## [10] highr_0.11           pkgconfig_2.0.3      Matrix_1.7-0         ## [13] desc_1.4.3           lifecycle_1.0.4      farver_2.1.2         ## [16] compiler_4.4.1       stringr_1.5.1        textshaping_0.4.0    ## [19] microbenchmark_1.5.0 munsell_0.5.1        codetools_0.2-20     ## [22] htmltools_0.5.8.1    sass_0.4.9           yaml_2.3.10          ## [25] pillar_1.9.0         pkgdown_2.1.0        jquerylib_0.1.4      ## [28] tidyr_1.3.1          MASS_7.3-60.2        openssl_2.2.1        ## [31] BiocParallel_1.38.0  cachem_1.1.0         boot_1.3-30          ## [34] RSpectra_0.16-2      Deriv_4.1.6          tidyselect_1.2.1     ## [37] digest_0.6.37        stringi_1.8.4        dplyr_1.1.4          ## [40] purrr_1.0.2          bookdown_0.40        labeling_0.4.3       ## [43] distances_0.1.11     cowplot_1.1.3        fastmap_1.2.0        ## [46] grid_4.4.1           colorspace_2.1-1     cli_3.6.3            ## [49] utf8_1.2.4           broom_1.0.6          withr_3.0.1          ## [52] scales_1.3.0         backports_1.5.0      rmarkdown_2.28       ## [55] modelr_0.1.11        reticulate_1.39.0    askpass_1.2.0        ## [58] ragg_1.3.3           png_0.1-8            evaluate_0.24.0      ## [61] knitr_1.48           rlang_1.1.4          glue_1.7.0           ## [64] BiocManager_1.30.25  jsonlite_1.8.8       R6_2.5.1             ## [67] systemfonts_1.1.0    fs_1.6.4"},{"path":"https://sydneybiox.github.io/wSIR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Max Woollard. Author, maintainer, contributor. Pratibha Panwar. Contributor. Linh Nghiem. Author, contributor. Shila Ghazanfar. Author, contributor.","code":""},{"path":"https://sydneybiox.github.io/wSIR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Woollard M, Nghiem L, Ghazanfar S (2024). wSIR: Weighted Sliced Inverse Regression (WSIR) supervised dimension reduction spatial transcriptomics single cell gene expression data. R package version 0.1.2, https://sydneybiox.github.io/wSIR/, https://sydneybiox.github.io/wSIR.","code":"@Manual{,   title = {wSIR: Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data},   author = {Max Woollard and Linh Nghiem and Shila Ghazanfar},   year = {2024},   note = {R package version 0.1.2,         https://sydneybiox.github.io/wSIR/},   url = {https://sydneybiox.github.io/wSIR}, }"},{"path":"https://sydneybiox.github.io/wSIR/index.html","id":"wsir","dir":"","previous_headings":"","what":"Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","title":"Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","text":"wSIR: Weighted Sliced Inverse Regression supervised dimension reduction spatial transcriptomics single cell gene expression data R package computation wSIR low-dimensional embedding gene expression data, using spatial coordinates. includes functions analyse resulting low-dimensional space, well mapping high-dimensional gene expression data low-dimensional space. function project new gene expression data (spatial coordinates unknown) low-dimensional space ability predict cell’s spatial location. overview method examples, see vignette website.","code":""},{"path":"https://sydneybiox.github.io/wSIR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Weighted Sliced Inverse Regression (WSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","text":"{r} library(devtools) install_github(\"SydneyBioX/wSIR\")","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":null,"dir":"Reference","previous_headings":"","what":"MouseGastrulationData — MouseData","title":"MouseGastrulationData — MouseData","text":"Data set consists spatial transcriptomics data mouse embryo. three samples, gene expression data (351 genes), spatial coordinates two-dimensional spatial plane, cell type labels. Sample 1 contains 19451 cells, sample 2 contains 14891 cells sample 3 contains 23194 cells. randomly sampled 20% cells datasets vignette examples stop data large. random sampling, 3848 cells sample 1, 2986 cells sample 2 4607 cells sample 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"MouseGastrulationData — MouseData","text":"sample1_exprs row cell sample 1 column expression level gene. sample1_coords row cell sample 1 column position two spatial axes. sample1_cell_types vector whose 'th entry contains cell type 'th cell sample 1. sample2_exprs row cell sample 2 column expression level gene. sample2_coords row cell sample 2 column position two spatial axes. sample2_cell_types vector whose 'th entry contains cell type 'th cell sample 2.  sample3_exprs row cell sample 3 column expression level gene. sample3_coords row cell sample 3 column position two spatial axes. sample3_cell_types vector, whose 'th entry contains cell type 'th cell sample 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MouseGastrulationData — MouseData","text":"Integration spatial single-cell transcriptomic data elucidates mouse organogenesis, Nature Biotechnology, 2022.  Webpage: https://www.nature.com/articles/s41587-021-01006-2","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"createWeightMatrix — createWeightMatrix","title":"createWeightMatrix — createWeightMatrix","text":"function create weight matrix given location cells, tile allocations desired spatial weighting strength. Weight matrix entries represent level spatial correlation pairs tiles.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"createWeightMatrix — createWeightMatrix","text":"","code":"createWeightMatrix(coords, labels, alpha = 4)"},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"createWeightMatrix — createWeightMatrix","text":"coords dataframe dimension n * 2. Column names c(\"x\", \"y\"). Spatial position cell. labels dataframe dimension n * 1, column name c(\"coordinate\"). Tile allocation cell. automatically created wSIR function. alpha numeric value giving strength spatial weighting matrix. alpha = 0 returns identity matrix equals SIR. Large alpha values tend entries towards 1. Default 4.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"createWeightMatrix — createWeightMatrix","text":"matrix containing weight value pairs tiles. value 0 1, 1 always diagonal.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":null,"dir":"Reference","previous_headings":"","what":"exploreWSIRParams function — exploreWSIRParams","title":"exploreWSIRParams function — exploreWSIRParams","text":"function used select optimal values parameters slices alpha weighted sliced inverse regression based provided gene expression data corresponding spatial coordinates. given evaluation metric, visualise performance WSIR varying function parameters based data, return optimal pair. pair slices alpha can used downstream tasks.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"exploreWSIRParams function — exploreWSIRParams","text":"","code":"exploreWSIRParams(   exprs,   coords,   samples = rep(1, nrow(coords)),   alpha_vals = c(0, 2, 4, 10),   slice_vals = c(5, 10, 15, 20),   varThreshold = 0.95,   maxDirections = 50,   metric = \"DC\",   nrep = 5,   param = MulticoreParam(workers = 1) )"},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"exploreWSIRParams function — exploreWSIRParams","text":"exprs matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). samples sample ID cell. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . alpha_vals vector numbers values parameter alpha use WSIR. 0 gives Sliced Inverse Regression (SIR) implementation, larger values represent stronger spatial correlation. Suggest use integers interpretability, can use non-integers. Values must non-negative. slice_vals vector integers values parameter slices use WSIR. Suggest maximum value vector around \\(\\sqrt{n/20}\\), upper bound ensures average least 10 cells per tile training set. varThreshold numeric proportion variance t(X_H) %*% W %*% X_H retain. Must 0 1. Default 0.95. Select higher threshold include dimensions, lower threshold include less dimensions. maxDirections integer maximum number directions include low-dimensional embedding. Default 50. metric evaluation metric use parameter tuning select optimal parameter combination. String, use \"DC\" use distance correlation, \"CD\" use correlation distances, \"ncol\" number dimensions low-dimensional embedding. Default \"DC\". nrep integer number train/test splits data perform. param parallel computing setup bplapply BiocParallel package. Default use single core, hence default value MulticoreParam(workers = 1)","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"exploreWSIRParams function — exploreWSIRParams","text":"List five slots, named \"plot\", \"message\", \"best_alpha\", \"best_slices\" \"results_dataframe\". \"plot\" shows average metric value across nrep iterations every combination parameters slices alpha. Larger circles slices/alpha combination indicates better performance pair values. one panel per evaluation metric selected \"metrics\" argument. \"message\" tells parameter combination highest metric value according selected metric. \"best_alpha\" returns integer best alpha values among values tested according selected metric. \"best_slices\" returns integer best slices value among values tested according selected metric. \"results_dataframe\" returns results dataframe used create \"plot\". dataframe length(alpha_vals)*length(slice_vals) rows, one combination parameters slices alpha. one column \"alpha\", one \"slices\" one evaluation metrics selected \"metrics\" argument. Column \"alpha\" includes value parameter alpha, column \"slices\" includes value parameter slices, metric column includes value specified metric, either Distance Correlation (\"DC\"), Correlation Distances (\"CD\"), number columns low-dimensional embedding (\"ncol\").","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"exploreWSIRParams function — exploreWSIRParams","text":"","code":"data(MouseData) explore_params = exploreWSIRParams(exprs = sample1_exprs,   coords = sample1_coords,   alpha_vals = c(0,2,4,8),   slice_vals = c(3,6,10)) #> Time difference of 1.628118 secs #> Time difference of 0.9077346 secs #> Time difference of 1.16018 secs #> Time difference of 0.7087882 secs #> Time difference of 0.9227376 secs #> Time difference of 1.018222 secs #> Time difference of 0.8205791 secs #> Time difference of 0.7322271 secs #> Time difference of 0.9753475 secs #> Time difference of 0.6757081 secs #> Time difference of 0.7184703 secs #> Time difference of 0.802834 secs explore_params$plot  explore_params$message #> [1] \"Optimal (alpha, slices) pair: (8, 3)\" best_alpha = explore_params$best_alpha best_slices = explore_params$best_slices wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = best_alpha,   slices = best_slices)"},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":null,"dir":"Reference","previous_headings":"","what":"findTopGenes — findTopGenes","title":"findTopGenes — findTopGenes","text":"function find visualise genes highest (absolute value) loading WSIR1. genes contribute first low-dimensional direction.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"findTopGenes — findTopGenes","text":"","code":"findTopGenes(WSIR, highest = 10, dirs = 1)"},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"findTopGenes — findTopGenes","text":"WSIR wsir object output wSIR function. analyse different DR method, ensure slot named 'directions' contains loadings matrix gene names rownames. highest integer many top genes like see. Recommend 20 ease visualisation. Default 10. dirs integer vector direction / directions want show/find top genes .","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"findTopGenes — findTopGenes","text":"List containing two slots. First named \"plot\" shows barplot top genes corresponding loadings. Second named \"genes\" vector genes highest (absolute value) loading low-dimensional direction 1. Length parameter highest.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"findTopGenes — findTopGenes","text":"","code":"data(MouseData)  wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8) # create top genes object top_genes_plot = top_genes_obj$plot # select plot top_genes_plot # print plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"generateUmapfromWSIR — generateUmapFromWSIR","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"function generate UMAP coordinates low-dimensional embedding gene expression data. coordinates can later plotted plotUmapFromWSIR function. two functions separate can generate UMAP points using function (may take long time), modify resulting plot much desired plotUmapFromWSIR function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"","code":"generateUmapFromWSIR(WSIR)"},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"WSIR wsir object output wSIR function. wish generate UMAP plots based DR methods, ensure slot named \"scores\" WSIR parameter contains low-dimensional representation exprs.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"matrix UMAP coordinates dimension nrow(coords) * 2. Output function can directly used input plot_umap function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 4) # create top genes object umap_plot = plotUmapFromWSIR(umap_coords = umap_coords,   exprs = sample1_exprs,   highest_genes = top_genes_obj,   n_genes = 4) umap_plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"plotUmapFromWSIR — plotUmapFromWSIR","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"function plot UMAP generated low-dimensional embedding gene expression data. points coloured value genes highest (absolute value) loading selected WSIR direction, default WSIR1.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"","code":"plotUmapFromWSIR(   exprs,   umap_coords,   highest_genes = NULL,   genes = NULL,   n_genes,   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"exprs matrix containing normalised gene expression data including n cells p genes, dimension n * p. umap_coords UMAP coordinates cell output generateUmapFromWSIR function. UMAP coordinates can based dimension reduction method, e.g UMAP coordinates computed WSIR dimension reduction gene expression data, PCs (principal components), low-dimensional matrix. Must matrix dimension nrow(exprs) * 2. highest_genes output findTopGenes function. Default NULL error message can easily thrown genes highest_genes provided. genes vector gene names (must colnames(exprs)) wish show UMAP plot. cells plots coloured expression values genes provide . Must provide either genes highest_genes parameter (): provide genes want visualise specific genes, provide highest_genes want visualise genes found important WSIR directions. Default NULL error message can easily thrown genes highest_genes provided. n_genes integer number genes like show. Default number unique genes highest_genes parameter number genes (vector) parameter genes. Use parameter want show important genes (e.g select top 4 n_genes = 4). ... additional parameters ggplot functions, e.g size (size points).","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"Grid umap plots n_genes number plots. shows cells UMAP generated low-dimensional gene expression data, coloured value genes found top_genes.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 4) # create top genes object umap_plot = plotUmapFromWSIR(umap_coords = umap_coords,   exprs = sample1_exprs,   highest_genes = top_genes_obj,   n_genes = 4) umap_plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"projectWSIR — projectWSIR","title":"projectWSIR — projectWSIR","text":"function project new gene expression data low-dimensional space","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"projectWSIR — projectWSIR","text":"","code":"projectWSIR(wsir, newdata)"},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"projectWSIR — projectWSIR","text":"wsir wsir object usually output wSIR function. want project new data low-dim space following different DR method, param wsir use list matrix loadings slot 2 (e.g PCA loadings) dimension p * d newdata matrix new gene expression data project low-dimensional space. Must p columns columns X argument used generate wsir.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"projectWSIR — projectWSIR","text":"matrix low-dimensional representation newdata gene expression data","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"projectWSIR — projectWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) sample2_low_dim_exprs = projectWSIR(wsir = wsir_obj, newdata = sample2_exprs)"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":null,"dir":"Reference","previous_headings":"","what":"sirCategorical — sirCategorical","title":"sirCategorical — sirCategorical","text":"function performs WSIR based provided gene expression data, tile allocations, weight matrix.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sirCategorical — sirCategorical","text":"","code":"sirCategorical(X, Y, directions = 50, W = NULL, varThreshold = 0.95)"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sirCategorical — sirCategorical","text":"X matrix normalised gene expression data n genes across p cells. Y dataframe 1 column named \"coordinate\" tile allocation cell. slices^2 unique tile IDs column. directions Integer specify maximum number directions retain thee low-dimensional embedding data. Use need certain number downstream task. W Weight matrix created createWeightMatrix. Entry (,j) represents spatial correlation level tiles j. diagonal values 1. provided, SIR implementation used. varThreshold numeric value specifying desired proportion variance retain. e.g 95% (default), number directions used eigenvalues add 95% sum eigenvalues.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sirCategorical — sirCategorical","text":"list outputs 5 named slots. output wSIR function: final step wSIR function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":null,"dir":"Reference","previous_headings":"","what":"sirPCA — sirPCA","title":"sirPCA — sirPCA","text":"function performs eigendecomposition (X^H)^t %% W %% (X^H), X^H matrix scaled slice means.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sirPCA — sirPCA","text":"","code":"sirPCA(   sliced_data,   directions,   W = diag(nrow(sliced_data)),   varThreshold = 0.95 )"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sirPCA — sirPCA","text":"sliced_data matrix scaled slice means directions integer, number directions want final low-dimensional Z. W matrix slice weights. Output createWeightMatrix function. varThreshold numeric proportion eigenvalues variance t(X_H) %% W %% X_H retain. Default 0.95. Select higher threshold include dimensions, lower threshold include less dimensions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sirPCA — sirPCA","text":"list containing: 1) \"eigenvectors\" matrix eigenvectors (X^H)^t %% W %% (X^H) 2) \"d\" integer, selected number dimensions 3) \"evalues\" vector eigenvalues (X^H)^t %% W %% (X^H)","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":null,"dir":"Reference","previous_headings":"","what":"slicerCategorical — slicerCategorical","title":"slicerCategorical — slicerCategorical","text":"function averages columns X matrix category single-column Y dataframe. used find average position within tile create weight matrix, well create tile means used eigendecomposition step SIR/wSIR.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"slicerCategorical — slicerCategorical","text":"","code":"slicerCategorical(X, Y)"},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"slicerCategorical — slicerCategorical","text":"X matrix dataframe containing columns average Y dataframe single column named 'coordinate'. column contains categorical variable tile ID cell.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"slicerCategorical — slicerCategorical","text":"dataframe containing averages column within category Y. Dimension h * p, h number categories Y p number columns X.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":null,"dir":"Reference","previous_headings":"","what":"spatialAllocator — spatialAllocator","title":"spatialAllocator — spatialAllocator","text":"function allocates cell tile based specified number tiles.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"spatialAllocator — spatialAllocator","text":"","code":"spatialAllocator(coords, slices = 3)"},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"spatialAllocator — spatialAllocator","text":"coords dataframe contains spatial position cell. Column names c(\"x\", \"y'). Must include row names integer 1 nrow(coords). automatically included wSIR function prior function. slices integer number slices along two spatial axes. number tiles slices^2 since two spatial axes. E.g set slices = 4 use 4^2 = 16 tiles. Default value 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"spatialAllocator — spatialAllocator","text":"output matrix containing slice belonging axis long format. used lapply(coords_split, spatialAllocator, slices) wSIR output dataframe cell's tile allocation \"coordinate\" column.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":null,"dir":"Reference","previous_headings":"","what":"visualiseWSIRDirections — visualiseWSIRDirections","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"function easily visualise low-dimensional gene expression data. function plots cell true spatial coordinates, coloured values WSIR1 / WSIR2 / ... . plots give intuition biological signals contained WSIR directions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"","code":"visualiseWSIRDirections(   coords,   WSIR,   dirs = 6,   mincol = \"blue\",   maxcol = \"red\" )"},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). WSIR wsir object output wSIR function. analyse different DR method, ensure slot named 'directions' contains loadings matrix gene names rownames. Must used coords parameter coords parameter function. dirs integer many low-dimensional directions like visualise. Recommend 10 ease visualisation. Default 6. mincol String colour low values low-dimensional directions. Personal choice user, default \"blue\". maxcol String colour high values low-dimensional directions. Personal choice user, default \"red\".","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"Grid plots dirs number plots. shows cells spatial positions coloured value first 'dirs' WSIR directions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object vis_obj = visualiseWSIRDirections(coords = sample1_coords, WSIR = wsir_obj, dirs = 8) # create visualisations vis_obj"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIR — wSIR","title":"wSIR — wSIR","text":"function perform supervised dimension reduction gene expression matrix using coordinates dataframe response value. Incorporates weighting mechanism SIR algorithm generate low-dimensional representation data allow projection new single-cell gene expression data low-dimensional space.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIR — wSIR","text":"","code":"wSIR(   X,   coords,   samples = rep(1, nrow(coords)),   slices = 8,   alpha = 4,   maxDirections = 50,   varThreshold = 0.95,   optim_params = TRUE,   alpha_vals = c(0, 1, 2, 4, 8, 12),   slice_vals = c(3, 5, 7, 10, 15, 20),   metric = \"DC\",   nrep = 5 )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIR — wSIR","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). samples sample ID cell. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . slices integer number slices axis tissue. example, slices = 4 creates 4 slices along spatial axis, yielding 4^2 = 16 tiles. Default 8, suggested minimum 3. Suggest tune parameter. alpha integer specify desired strength spatial correlation. Suggest tune parameter testing dataset among e.g values c(0,2,4,8). alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations. maxDirections integer upper limit number directions include low-dimensional space. Use need less certain number dimensions downstream analyes varThreshold numeric proportion eigenvalues variance t(X_H) %% W %% X_H retain. Default 0.95. Select higher threshold include dimensions, lower threshold include less dimensions. optim_params logical, like wSIR automatically optimise parameters slices alpha based either distance correlation correlation distances evaluation metric. Default TRUE. downstream task quite different either metrics, suggest tune two parameters using specific task evaluation metric. case, determine optimal slices alpha values use relevant function, setting optim_params = FALSE. alpha_vals optim_params = TRUE, values alpha optimise WSIR. 0 gives Sliced Inverse Regression (SIR) implementation, larger values represent stronger spatial correlation. Suggest use integers interpretability, can use non-integers. Values must non-negative. slice_vals optim_params = TRUE, values slices optimise WSIR. Suggest maximum value vector around \\(\\sqrt{n/20}\\), upper bound ensures average least 10 cells per tile training set. metric optim_params = TRUE, evaluation metric use parameter tuning. String, either \"DC\" use distance correlation \"CD\" use correlation distances. Default \"DC\". nrep optim_params = TRUE, integer number train/test splits data perform optimisation parameters slices alpha.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIR — wSIR","text":"wSIR object list containing 5 (named) positions. 1) scores matrix containing low-dimensional representation X wSIR algorithm. Dimension n * d, d chosen include least varThreshold proportion variance. 2) directions matrix containing WSIR directions, dimension p * d. Use project new data low-dimensional space via X_new %% directions. 3) estd integer stating many dimensions computed low-dimensional space needed account varThreshold proportion variance. number dimensions scores. 4) W matrix weight matrix cells_weight_matrix2 function 5) evalues vector containing p eigenvalues t(X_H) %% W %*% X_H. varThreshold parameter works evalues, e.g first j directions included sum first j evalues equals 0.95% sum evalues.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wSIR — wSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = TRUE,   alpha_vals = c(0,2,4),   slice_vals = c(3,6,10),   metric = \"CD\",   nrep = 1) # create wsir object #> Time difference of 0.134021 secs #> Time difference of 0.1995811 secs #> Time difference of 0.2074399 secs #> Time difference of 0.1596627 secs #> Time difference of 0.1758702 secs #> Time difference of 0.1792307 secs #> Time difference of 0.1397426 secs #> Time difference of 0.1735837 secs #> Time difference of 0.1792359 secs"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIROptimisation — wSIROptimisation","title":"wSIROptimisation — wSIROptimisation","text":"function used calculate validation metric best tuning parameters selected","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIROptimisation — wSIROptimisation","text":"","code":"wSIROptimisation(   exprs_train,   coords_train,   exprs_test,   coords_test,   samples_train,   slices,   alpha,   maxDirections,   varThreshold,   metrics = c(\"CD\", \"DC\", \"ncol\") )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIROptimisation — wSIROptimisation","text":"exprs_train matrix normalised gene expression training data. coords_train dataframe containing spatial positions cells 2D space  validation data. Dimension n * 2. Column names must c(\"x\", \"y\"). exprs_test matrix normalised gene expression validation data. samples_train sample ID cell training data. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . slices integer number slices axis tissue. example, slices = 4 creates 4 slices along spatial axis, yielding 4^2 = 16 tiles. Default 8, suggested minimum 3. Suggest tune parameter using exploreWSIRParams() function. alpha integer specify desired strength spatial correlation. alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations. Default value 4, weight matrix value pair tiles inversely proportional physical distance . Suggest tune parameter using exploreWSIRParams() function. maxDirections integer maximum number directions include low-dimenensional embedding. Default 50. varThreshold numeric proportion variance t(X_H) %*% W %*% X_H retain. Must 0 1. Default 0.95. Select higher threshold include dimensions, lower threshold include less dimensions. metrics evaluation metrics use parameter tuning. String, options : \"DC\" use distance correlation; \"CD\" use correlation distances; \"ncol\" use number columns low-dimensional embedding. Default three, specified metrics = c(\"DC\", \"CD\", \"ncol\").","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIROptimisation — wSIROptimisation","text":"Average metric value selected metric(s) train/test split.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIR_specified_params — wSIRSpecifiedParams","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"wSIR function specified parameters alpha, slices. ued internally wSIROptimisation wSIR functions, specific values alpha slices.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"","code":"wSIRSpecifiedParams(   X,   coords,   samples = rep(1, nrow(coords)),   slices = 8,   alpha = 4,   maxDirections = 50,   varThreshold = 0.95 )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). samples sample ID cell. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . slices integer number slices axis tissue. example, slices = 4 creates 4 slices along spatial axis, yielding 4^2 = 16 tiles. Default 8, suggested minimum 3. Suggest tune parameter. alpha integer specify desired strength spatial correlation. Suggest tune parameter testing dataset among e.g values c(0,2,4,8). alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations. maxDirections integer upper limit number directions include low-dimensional space. Use need less certain number dimensions downstream analyes varThreshold numeric proportion eigenvalues variance t(X_H) %% W %% X_H retain. Default 0.95. Select higher threshold include dimensions, lower threshold include less dimensions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"wSIR object list containing 5 (named) positions. 1) scores matrix containing low-dimensional representation X wSIR algorithm. Dimension n * d, d chosen include least varThreshold proportion variance. 2) directions matrix containing WSIR directions, dimension p * d. Use project new data low-dimensional space via X_new %% directions. 3) estd integer stating many dimensions computed low-dimensional space needed account varThreshold proportion variance. number dimensions scores. 4) W matrix weight matrix createWeightMatrix function 5) evalues vector containing p eigenvalues t(X_H) %% W %*% X_H. varThreshold parameter works evalues, e.g first j directions included sum first j evalues equals 0.95% sum evalues.","code":""}]
+[{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Weighted Sliced Inverse Regression (WSIR)","text":"Weighted Sliced Inverse Regression (wSIR) supervised dimension reduction technique spatial transcriptomics data. method uses cell’s gene expression values covariates spatial position response. allows us create low-dimensional representation gene expression data retains information spatial patterns present gene expression data. resulting mapping gene expression data spatially aware low-dimensional embedding can used project new single-cell gene expression data low-dimensional space preserves ability predict cell’s spatial location low-dimensional embedding.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"method-overview","dir":"Articles","previous_headings":"Introduction","what":"Method Overview","title":"Weighted Sliced Inverse Regression (WSIR)","text":"wSIR extension supervised dimension reduction technique Sliced Inverse Regression (SIR). SIR existing supervised dimension reduction method works grouping together observations similar values response. spatial transcriptomics data, means grouping cells certain number tiles based spatial position. example, use 4 tiles, cells top right quadrant image/tissue go one group, top left another, . groups summarised averaging expression level cells group genes. , eigendecomposition performed resulting matrix tile-gene means, returning SIR directions SIR scores. motivation behind wSIR SIR uses cell’s spatial position separating cells given number groups/tiles. groups created, lose fact groups may spatially related (come adjacent tiles) groups (come opposite sides tissue). wSIR uses weight matrix incorporate spatial correlation pairs cells SIR algorithm. matrix dimension H*H, H number tiles, (,j)th entry represents distance tiles j. matrix incorporated eigendeomposition step. wSIR output structure SIR output.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"vignette-overview","dir":"Articles","previous_headings":"Introduction","what":"Vignette Overview","title":"Weighted Sliced Inverse Regression (WSIR)","text":"vignette, demonstrate use WSIR obtain low-dimensional embedding gene expression data. explore embedding using package’s built-functions. However, low-dimensional matrix importantly used downstream tasks benefit lower-dimensional representation gene expression data preserves information cell’s spatial location. perform basic downstream analysis demonstrate practicality wSIR.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Weighted Sliced Inverse Regression (WSIR)","text":"use data Lohoff et al, 2021. code allow download mouse data using MouseGastrulationData scran R packages. randomly sample 20% cells three biological replicate samples. vignette examples function wSIR, simply load data already saved data/MouseData.rda.","code":"# Packages needed to download data #library(scran) # for logNormCounts #library(MouseGastrulationData) # to download the data for this vignette  set.seed(2024)  seqfish_data_sample1 <- LohoffSeqFISHData(samples = c(1,2)) seqfish_data_sample1 = logNormCounts(seqfish_data_sample1) # log transform variance stabilising rownames(seqfish_data_sample1) <- rowData(seqfish_data_sample1)[,\"SYMBOL\"] # change rownames to gene symbols that are consistent sample1_exprs = t(assay(seqfish_data_sample1, \"logcounts\")) # extract matrix of gene expressions sample1_coords = spatialCoords(seqfish_data_sample1)[,1:2] %>% as.data.frame() colnames(sample1_coords) = c(\"x\", \"y\")  seqfish_data_sample2 <- LohoffSeqFISHData(samples = c(3,4)) seqfish_data_sample2 = logNormCounts(seqfish_data_sample2) rownames(seqfish_data_sample2) <- rowData(seqfish_data_sample2)[,\"SYMBOL\"] sample2_exprs = t(assay(seqfish_data_sample2, \"logcounts\")) sample2_coords = spatialCoords(seqfish_data_sample2)[,1:2] %>% as.data.frame() colnames(sample2_coords) = c(\"x\", \"y\")  seqfish_data_sample3 <- LohoffSeqFISHData(samples = c(5,6)) seqfish_data_sample3 = logNormCounts(seqfish_data_sample3) rownames(seqfish_data_sample3) <- rowData(seqfish_data_sample3)[,\"SYMBOL\"] sample3_exprs = t(assay(seqfish_data_sample3, \"logcounts\")) sample3_coords = spatialCoords(seqfish_data_sample3)[,1:2] %>% as.data.frame() colnames(sample3_coords) = c(\"x\", \"y\")  keep1 = sample(c(TRUE, FALSE), nrow(sample1_exprs), replace = TRUE, prob = c(0.2, 0.8)) keep2 = sample(c(TRUE, FALSE), nrow(sample2_exprs), replace = TRUE, prob = c(0.2, 0.8)) keep3 = sample(c(TRUE, FALSE), nrow(sample3_exprs), replace = TRUE, prob = c(0.2, 0.8))  sample1_exprs = sample1_exprs[keep1,] sample1_coords = sample1_coords[keep1,] sample2_exprs = sample2_exprs[keep2,] sample2_coords = sample2_coords[keep2,] sample3_exprs = sample3_exprs[keep3,] sample3_coords = sample3_coords[keep3,]  sample1_cell_types = seqfish_data_sample1$celltype[keep1] sample2_cell_types = seqfish_data_sample2$celltype[keep2] sample3_cell_types = seqfish_data_sample3$celltype[keep3]  save(sample1_exprs, sample1_coords, sample1_cell_types,       sample2_exprs, sample2_coords, sample2_cell_types,       sample3_exprs, sample3_coords, sample3_cell_types,       file = \"../data/MouseData.rda\", compress = \"xz\") data(MouseData)"},{"path":[]},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"parameter-study","dir":"Articles","previous_headings":"Supervised dimension reduction with wSIR","what":"Parameter Study","title":"Weighted Sliced Inverse Regression (WSIR)","text":"wSIR contains two parameters, alpha slices. Parameter slices number groups along spatial axis split observations wSIR algorithm. slices means pick spatial information gene expression data, risk overfitting training set. Parameter alpha modifies strength weight matrix. alpha = 0 equivalent spatial weighting, meaning weight matrix becomes identity matrix. equivalent SIR. Larger alpha values means weight given spatial correlation rather gene expression differences alone computation wSIR directions. function exploreWSIRParams performs wSIR many choices alpha slices identify appropriate parameters moving forward. , use sample 1 . parameters tuned reasonable values. function exploreWSIRParams computes visualises performance wSIR given combinations slices alpha. performance computed following procedure: 1) combination slices alpha, split data 50% train 50% test (train test halves include gene expression data cell coordinates). 2) Perform wSIR training set using current combination slices alpha. 3) Project gene expression data testing set low-dimensional space using wSIR results step 2. 4) Evaluate wSIR’s performance computing either distance correlation (“DC”, default), correlation distances (“CD”) projected gene expression data test set test coordinates, according parameter metric. 5) Repeat steps 2-4 done nrep times (nrep parameter whose default 5). Calculate average metric value nrep iterations combination slices alpha. 6) Repeat steps 1-5 combinations slices alpha obtain average metric value combination. 7) Return combination slices alpha highest average metric value display plot showing performance every combination parameters. Note: key advantage wSIR SIR parameter robustness. , can see SIR’sperformance deteriorates use larger values slices. However, wSIR’s performance relatively stable vary slices alpha across reasonable values (e.g. among default values optimise ). following plot, metric value becomes smaller increase number slices α=0\\alpha = 0 (corresponds SIR). Performance stable non-zero alpha slice combinations (correspond wSIR).","code":"a <- Sys.time() optim_obj = exploreWSIRParams(X = sample1_exprs,                                coords = sample1_coords,                                alpha_vals = c(0,.5, 1,2,4,8),                                slice_vals = c(5,10,15),                               nrep = 20, metric = \"CD\") ## Time difference of 13.9127 secs ## Time difference of 13.72888 secs ## Time difference of 14.08977 secs ## Time difference of 12.80965 secs ## Time difference of 14.11709 secs ## Time difference of 14.00368 secs ## Time difference of 12.99858 secs ## Time difference of 13.8238 secs ## Time difference of 13.7851 secs ## Time difference of 12.24654 secs ## Time difference of 13.42864 secs ## Time difference of 13.662 secs ## Time difference of 12.32729 secs ## Time difference of 13.384 secs ## Time difference of 13.77942 secs ## Time difference of 12.11121 secs ## Time difference of 13.42403 secs ## Time difference of 13.60149 secs Sys.time()-a ## Time difference of 4.029568 mins optim_obj$plot"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-computation","dir":"Articles","previous_headings":"Supervised dimension reduction with wSIR","what":"wSIR Computation","title":"Weighted Sliced Inverse Regression (WSIR)","text":"next perform wSIR using optimal parameter combination found previous section. use gene expression matrix spatial coordinates sample 1 . returns list results 5 (named) slots, whose details can found ?wSIR::wSIR.","code":"wsir_obj = wSIR(X = sample1_exprs,                  coords = sample1_coords,                  slices = optim_obj$best_slices,                  alpha = optim_obj$best_alpha,                 optim_params = FALSE)  names(wsir_obj) ## [1] \"scores\"     \"directions\" \"estd\"       \"W\"          \"evalues\""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-results-analysis","dir":"Articles","previous_headings":"","what":"wSIR Results Analysis","title":"Weighted Sliced Inverse Regression (WSIR)","text":"section, demonstrate use built-analysis functions better understand wSIR creates spatially-informed low-dimensional embedding. functions use wSIR result input. , use output previous section, meaning studying result performing wSIR sample 1 .","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"wsir-top-genes","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"wSIR Top Genes","title":"Weighted Sliced Inverse Regression (WSIR)","text":"findTopGenes function finds plots genes highest loading specified wSIR directions (default direction 1). gene high loading (terms magnitude), important wSIR direction. Since wSIR directions designed retain information cell’s spatial position, genes high loading spatially-related genes. plot , can see genes highest loading wSIR direction 1. useful gives us intuition wSIR creates low-dimensional embedding. can see genes known spatial genes (e.g. Cdx2, Hox-), expect see. can simultaneously plot top genes multiple directions, utilise ","code":"top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8) # create top genes object top_genes_plot = top_genes_obj$plot # select plot top_genes_plot # print plot top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8, dirs = 2:4) top_genes_plot = top_genes_obj$plot top_genes_plot"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"visualising-wsir-scores","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"Visualising wSIR Scores","title":"Weighted Sliced Inverse Regression (WSIR)","text":"visualiseWSIRDirections function plots cell spatial position, coloured value specified wSIR columns. gives us understanding column low-dimensional embedding represents. , visualise cells spatial positions, coloured 5 wSIR directions top left plot illustrates , example, wSIR direction 1 captures information “y” spatial axis, since cells higher “y” coordinate low wSIR1 value, cells lower “y” coordinate higher wSIR1 value. wSIR2 shown next plot (one titled “2”), can see wSIR column two appears capture information “x” spatial coordinate. remaining three wSIR columns contain information cell types, can tell regions high low wSIR column values spread across tissue.","code":"vis_obj = visualiseWSIRDirections(coords = sample1_coords, WSIR = wsir_obj, dirs = 8) # create visualisations vis_obj"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"umap-on-low-dimensional-embedding","dir":"Articles","previous_headings":"wSIR Results Analysis","what":"UMAP on low-dimensional embedding","title":"Weighted Sliced Inverse Regression (WSIR)","text":"two functions generateUmapFromWSIR plotUmapFromWSIR create display UMAP dimension reduction calculated wSIR low-dimensional embedding. can colour UMAP plot (point represents cell) value various genes interest. visualises structure wSIR dimension reduction space, useful gain intuition space represents. Specifically, can see wSIR space contains neighbourhoods high expression specific genes, thus better understanding space made. specify genes like include, can use output findTopGenes function , finds spatially-related genes ranking highest loading relevant wSIR directions. output value highest_genes parameter. Otherwise, also specify genes interest specific genes like visualise. example, wanted visualise expression distribution Cdx2 Hoxb4, use genes = c(\"Cdx2\", \"Hoxb4\") argument plotUmapFromWSIR (leave highest_genes blank). , use UMAP function visualise wSIR space computed gene expression data sample 1. colour cell values 6 genes highest value wSIR direction 1 (found findTopGenes function previously). can see genes, specific regions high expression UMAP plots, suggesting wSIR space separates cells based expression genes.","code":"umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) umap_plots = plotUmapFromWSIR(X = sample1_exprs,                               umap_coords = umap_coords,                               highest_genes = top_genes_obj,                               n_genes = 6) umap_plots"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"projection-of-new-data-with-wsir","dir":"Articles","previous_headings":"","what":"Projection of new data with wSIR","title":"Weighted Sliced Inverse Regression (WSIR)","text":"key functionality wSIR package ability project new single-cell data wSIR low-dimensional space. allow low-dimensional representation gene expression data contains information cell’s spatial position even though access spatial coordinates new data. low-dimensional wSIR embedding especially useful downstream applications, like spatial alignment spatial clustering (don’t spatial coordinates). , demonstrate steps , well specific application. projection example, perform wSIR spatial transcriptomics dataset includes gene expression data spatial coordinates. project “single-cell” dataset, contains gene expression matrix, wSIR low-dimensional space.","code":""},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"single-sample-spatial-dataset-single-sample-single-cell-dataset","dir":"Articles","previous_headings":"Projection of new data with wSIR","what":"Single-sample spatial dataset, single-sample single-cell dataset","title":"Weighted Sliced Inverse Regression (WSIR)","text":", demonstrate steps project new sample single-cell dataset wSIR low-dimensional space already performed wSIR spatial transcriptomics dataset first sample. already performed wSIR sample 1, project sample 2’s gene expression matrix wSIR low-dimensional space, therefore ability predict sample 2’s (unknown stage) spatial locations. Check dimension sample 2’s low-dimensional gene expression data: Observe sample 2’s low-dimensional gene expression data: low-dimensional gene expression data can used later tasks benefit low-dimensional embedding gene expression data samples, rather just gene expression data.","code":"sample2_low_dim_exprs = projectWSIR(wsir = wsir_obj, newdata = sample2_exprs) dim(sample2_low_dim_exprs) ## [1] 2986   20 head(sample2_low_dim_exprs) ##           [,1]       [,2]       [,3]       [,4]       [,5]       [,6] ## [1,] 1.0930341 -0.1443871 -1.5090562  1.5873578 -1.3890866  0.3799610 ## [2,] 0.4680595  0.5327497 -0.3728135  1.0015734  0.8920196  0.1513758 ## [3,] 1.2711455 -0.7397591 -1.4112419 -0.8839003  0.2468790  0.3832880 ## [4,] 1.7531946 -0.1143880 -0.7064318 -0.3602051  0.4724328 -0.2961441 ## [5,] 2.2381875 -0.5924558 -0.3165188  0.2141844  0.7782123 -0.2621179 ## [6,] 1.3299499 -0.8290736  0.6474762  0.6397734  0.5299646 -0.9353155 ##            [,7]       [,8]        [,9]      [,10]       [,11]      [,12] ## [1,] -3.4678998 0.56681244 -0.81551059  1.1852559 -0.73526941 -1.0785081 ## [2,] -1.0017729 1.95474647 -0.43692515 -0.1603876 -0.15958819 -0.4427703 ## [3,] -2.3845440 0.08795112  1.16277476  0.2721857  0.28169183  0.4604845 ## [4,] -2.2187255 0.65176353  0.20339372  1.2128839  1.07096226  0.4317445 ## [5,] -0.5669189 1.84121180  0.06402206 -0.3221178  0.02488601  0.3517457 ## [6,] -1.0554684 1.07326688 -0.42296176 -0.3972216 -0.72847203  0.7158252 ##            [,13]      [,14]        [,15]       [,16]      [,17]       [,18] ## [1,]  0.22645766  0.7414184  0.885021243  1.74928451  0.4172434 -0.36172912 ## [2,] -0.11743827 -1.3097013  0.335409580 -0.19737044  1.9184451  0.06779531 ## [3,] -0.09524678 -0.7460559 -0.583083970  0.25165178  0.6701605  0.43165415 ## [4,] -0.04954717 -1.0375261 -0.005446275 -0.59153455  0.4787165  0.06904808 ## [5,]  1.03117637 -0.4426845 -0.023429569  0.03480782 -0.1772584 -0.21056960 ## [6,]  0.35505985 -0.2426742  1.098536792  0.38752300  0.1341204  0.42348720 ##           [,19]       [,20] ## [1,]  0.2994094  0.71759073 ## [2,] -0.3500336 -0.06717946 ## [3,]  0.6338821  0.32693292 ## [4,]  0.6517039  0.23217160 ## [5,] -1.2366838  0.41684787 ## [6,] -0.9978965  0.58964001"},{"path":"https://sydneybiox.github.io/wSIR/articles/wSIR_vignette.html","id":"multi-sample-spatial-dataset-single-sample-single-cell-dataset","dir":"Articles","previous_headings":"Projection of new data with wSIR","what":"Multi-sample spatial dataset, single-sample single-cell dataset","title":"Weighted Sliced Inverse Regression (WSIR)","text":", perform wSIR samples 1 2 together. requires gene expression matrices samples joined together one matrix, coords dataframes joined one dataframe. concatenation using rbind. specify sample IDs row joined expression matrix coordinates dataframe using samples argument. vector “1” row sample 1 “2” row sample 2. use resulting wSIR output project gene expression data sample 3 low-dimensional space. check dimension resulting matrix. low-dimensional matrix can used downstream tasks benefit low-dimensional embedding sample 3’s gene expression matrix contains information cell’s location. Examples downstream use include spatial alignment single-cell spatial gene expression data via Tangram, using wSIR scores input rather (unreduced) gene expression matrix. example simple application using wSIR scores input KNN cell type classification algorithm. demonstrated , using ‘knn’ function ‘class’ package. code, use wSIR scores input simple KNN-based cell type classification tool. print tail prediction vector, demonstrating can use wSIR-based low-dimensional gene expression data new sample step realistic analysis pipeline.","code":"wsir_obj_samples12 <- wSIR(X = rbind(sample1_exprs, sample2_exprs),                            coords = rbind(sample1_coords, sample2_coords),                            samples = c(rep(1, nrow(sample1_coords)), rep(2, nrow(sample2_coords))),                            slices = optim_obj$best_slices,                             alpha = optim_obj$best_alpha,                            optim_params = FALSE)  sample3_low_dim_exprs <- projectWSIR(wsir = wsir_obj_samples12, newdata = sample3_exprs) dim(sample3_low_dim_exprs) ## [1] 4607   29 samples12_cell_types = append(sample1_cell_types, sample2_cell_types)  knn_classification_object = knn(train = wsir_obj_samples12$scores,                                  test = sample3_low_dim_exprs,                                 cl = samples12_cell_types,                                 k = 10)  tail(knn_classification_object) ## [1] Presomitic mesoderm Presomitic mesoderm Presomitic mesoderm ## [4] Presomitic mesoderm Presomitic mesoderm Presomitic mesoderm ## 24 Levels: Allantois Anterior somitic tissues ... Surface ectoderm sessionInfo() ## R version 4.4.1 (2024-06-14) ## Platform: x86_64-pc-linux-gnu ## Running under: Ubuntu 22.04.5 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        ##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    ##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           ## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] class_7.3-22       umap_0.2.10.0      vctrs_0.6.5        doBy_4.6.22        ##  [5] Rfast_2.1.0        RcppParallel_5.1.9 RcppZiggurat_0.1.6 Rcpp_1.0.13        ##  [9] ggplot2_3.5.1      magrittr_2.0.3     wSIR_0.1.3         BiocStyle_2.32.1   ##  ## loaded via a namespace (and not attached): ##  [1] gtable_0.3.5         xfun_0.47            bslib_0.8.0          ##  [4] lattice_0.22-6       tools_4.4.1          generics_0.1.3       ##  [7] parallel_4.4.1       tibble_3.2.1         fansi_1.0.6          ## [10] highr_0.11           pkgconfig_2.0.3      Matrix_1.7-0         ## [13] desc_1.4.3           lifecycle_1.0.4      farver_2.1.2         ## [16] compiler_4.4.1       stringr_1.5.1        textshaping_0.4.0    ## [19] microbenchmark_1.5.0 munsell_0.5.1        codetools_0.2-20     ## [22] htmltools_0.5.8.1    sass_0.4.9           yaml_2.3.10          ## [25] pillar_1.9.0         pkgdown_2.1.1        jquerylib_0.1.4      ## [28] tidyr_1.3.1          MASS_7.3-60.2        openssl_2.2.1        ## [31] BiocParallel_1.38.0  cachem_1.1.0         boot_1.3-30          ## [34] RSpectra_0.16-2      Deriv_4.1.6          tidyselect_1.2.1     ## [37] digest_0.6.37        stringi_1.8.4        dplyr_1.1.4          ## [40] purrr_1.0.2          bookdown_0.40        labeling_0.4.3       ## [43] distances_0.1.11     cowplot_1.1.3        fastmap_1.2.0        ## [46] grid_4.4.1           colorspace_2.1-1     cli_3.6.3            ## [49] utf8_1.2.4           broom_1.0.6          withr_3.0.1          ## [52] scales_1.3.0         backports_1.5.0      rmarkdown_2.28       ## [55] modelr_0.1.11        reticulate_1.39.0    askpass_1.2.0        ## [58] ragg_1.3.3           png_0.1-8            evaluate_1.0.0       ## [61] knitr_1.48           rlang_1.1.4          glue_1.7.0           ## [64] BiocManager_1.30.25  jsonlite_1.8.8       R6_2.5.1             ## [67] systemfonts_1.1.0    fs_1.6.4"},{"path":"https://sydneybiox.github.io/wSIR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Max Woollard. Author, maintainer, contributor. Pratibha Panwar. Contributor. Linh Nghiem. Author, contributor. Shila Ghazanfar. Author, contributor.","code":""},{"path":"https://sydneybiox.github.io/wSIR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Woollard M, Nghiem L, Ghazanfar S (2024). wSIR: Weighted Sliced Inverse Regression (wSIR) supervised dimension reduction spatial transcriptomics single cell gene expression data. R package version 0.1.3, https://sydneybiox.github.io/wSIR.","code":"@Manual{,   title = {wSIR: Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data},   author = {Max Woollard and Linh Nghiem and Shila Ghazanfar},   year = {2024},   note = {R package version 0.1.3},   url = {https://sydneybiox.github.io/wSIR}, }"},{"path":"https://sydneybiox.github.io/wSIR/index.html","id":"wsir","dir":"","previous_headings":"","what":"Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","title":"Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","text":"wSIR: Weighted Sliced Inverse Regression supervised dimension reduction spatial transcriptomics single cell gene expression data R package computation wSIR low-dimensional embedding gene expression data, using spatial coordinates. includes functions analyse resulting low-dimensional space, well mapping high-dimensional gene expression data low-dimensional space. function project new gene expression data (spatial coordinates unknown) low-dimensional space ability predict cell’s spatial location. overview method examples, see vignette website.","code":""},{"path":"https://sydneybiox.github.io/wSIR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Weighted Sliced Inverse Regression (wSIR) for supervised dimension reduction of spatial transcriptomics and single cell gene expression data","text":"{r} library(devtools) install_github(\"SydneyBioX/wSIR\")","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":null,"dir":"Reference","previous_headings":"","what":"MouseGastrulationData — MouseData","title":"MouseGastrulationData — MouseData","text":"Data set consists spatial transcriptomics data mouse embryo. three samples, gene expression data (351 genes), spatial coordinates two-dimensional spatial plane, cell type labels. Sample 1 contains 19451 cells, sample 2 contains 14891 cells sample 3 contains 23194 cells. randomly sampled 20% cells datasets vignette examples stop data large. random sampling, 3848 cells sample 1, 2986 cells sample 2 4607 cells sample 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"MouseGastrulationData — MouseData","text":"sample1_exprs row cell sample 1 column expression level gene. sample1_coords row cell sample 1 column position two spatial axes. sample1_cell_types vector whose 'th entry contains cell type 'th cell sample 1. sample2_exprs row cell sample 2 column expression level gene. sample2_coords row cell sample 2 column position two spatial axes. sample2_cell_types vector whose 'th entry contains cell type 'th cell sample 2.  sample3_exprs row cell sample 3 column expression level gene. sample3_coords row cell sample 3 column position two spatial axes. sample3_cell_types vector, whose 'th entry contains cell type 'th cell sample 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/MouseData.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MouseGastrulationData — MouseData","text":"Integration spatial single-cell transcriptomic data elucidates mouse organogenesis, Nature Biotechnology, 2022.  Webpage: https://www.nature.com/articles/s41587-021-01006-2","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"createWeightMatrix — createWeightMatrix","title":"createWeightMatrix — createWeightMatrix","text":"function create weight matrix given location cells, tile allocations desired spatial weighting strength. Weight matrix entries represent level spatial correlation pairs tiles.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"createWeightMatrix — createWeightMatrix","text":"","code":"createWeightMatrix(coords, labels, alpha = 4)"},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"createWeightMatrix — createWeightMatrix","text":"coords dataframe dimension n * 2. Column names c(\"x\", \"y\"). Spatial position cell. labels dataframe dimension n * 1, column name c(\"coordinate\"). Tile allocation cell. automatically created wSIR function. alpha numeric value giving strength spatial weighting matrix. alpha = 0 returns identity matrix equals SIR. Large alpha values tend entries towards 1. Default 4.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/createWeightMatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"createWeightMatrix — createWeightMatrix","text":"matrix containing weight value pairs tiles. value 0 1, 1 always diagonal.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":null,"dir":"Reference","previous_headings":"","what":"exploreWSIRParams function — exploreWSIRParams","title":"exploreWSIRParams function — exploreWSIRParams","text":"function used select optimal values parameters slices alpha weighted sliced inverse regression based provided gene expression data corresponding spatial coordinates. given evaluation metric, visualise performance WSIR varying function parameters based data, return optimal pair. pair slices alpha can used downstream tasks.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"exploreWSIRParams function — exploreWSIRParams","text":"","code":"exploreWSIRParams(   X,   coords,   samples = rep(1, nrow(coords)),   alpha_vals = c(0, 2, 4, 10),   slice_vals = c(5, 10, 15, 20),   metric = \"DC\",   nrep = 5,   param = MulticoreParam(workers = 1),   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"exploreWSIRParams function — exploreWSIRParams","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). samples sample ID cell. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . alpha_vals vector numbers values parameter alpha use WSIR. 0 gives Sliced Inverse Regression (SIR) implementation, larger values represent stronger spatial correlation. Suggest use integers interpretability, can use non-integers. Values must non-negative. slice_vals vector integers values parameter slices use WSIR. Suggest maximum value vector around \\(\\sqrt{n/20}\\), upper bound ensures average least 10 cells per tile training set. metric character single value. Evaluation metric use parameter tuning select optimal parameter combination. String, use \"DC\" use distance correlation, \"CD\" use correlation distances, \"ncol\" number dimensions low-dimensional embedding. Default \"DC\". nrep integer number train/test splits data perform. param parallel computing setup bplapply BiocParallel package. Default use single core, hence default value MulticoreParam(workers = 1) ... arguments passed wSIROptimisation","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"exploreWSIRParams function — exploreWSIRParams","text":"List five slots, named \"plot\", \"message\", \"best_alpha\", \"best_slices\" \"results_dataframe\". \"plot\" shows average metric value across nrep iterations every combination parameters slices alpha. Larger circles slices/alpha combination indicates better performance pair values. one panel per evaluation metric selected \"metrics\" argument. \"message\" tells parameter combination highest metric value according selected metric. \"best_alpha\" returns integer best alpha values among values tested according selected metric. \"best_slices\" returns integer best slices value among values tested according selected metric. \"results_dataframe\" returns results dataframe used create \"plot\". dataframe length(alpha_vals)*length(slice_vals) rows, one combination parameters slices alpha. one column \"alpha\", one \"slices\" one evaluation metrics selected \"metrics\" argument. Column \"alpha\" includes value parameter alpha, column \"slices\" includes value parameter slices, metric column includes value specified metric, either Distance Correlation (\"DC\"), Correlation Distances (\"CD\"), number columns low-dimensional embedding (\"ncol\").","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/exploreWSIRParams.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"exploreWSIRParams function — exploreWSIRParams","text":"","code":"data(MouseData) explore_params = exploreWSIRParams(X = sample1_exprs,   coords = sample1_coords,   alpha_vals = c(0,2,4,8),   slice_vals = c(3,6,10)) #> Time difference of 1.569175 secs #> Time difference of 0.8919282 secs #> Time difference of 1.118085 secs #> Time difference of 0.8686008 secs #> Time difference of 1.062988 secs #> Time difference of 1.002786 secs #> Time difference of 0.919801 secs #> Time difference of 0.8633406 secs #> Time difference of 1.124423 secs #> Time difference of 0.7145364 secs #> Time difference of 0.8456833 secs #> Time difference of 1.124386 secs explore_params$plot  explore_params$message #> [1] \"Optimal (alpha, slices) pair: (0, 3)\" best_alpha = explore_params$best_alpha best_slices = explore_params$best_slices wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = best_alpha,   slices = best_slices)"},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":null,"dir":"Reference","previous_headings":"","what":"findTopGenes — findTopGenes","title":"findTopGenes — findTopGenes","text":"function find visualise genes highest (absolute value) loading WSIR1. genes contribute first low-dimensional direction.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"findTopGenes — findTopGenes","text":"","code":"findTopGenes(WSIR, highest = 10, dirs = 1)"},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"findTopGenes — findTopGenes","text":"WSIR wsir object output wSIR function. analyse different DR method, ensure slot named 'directions' contains loadings matrix gene names rownames. highest integer many top genes like see. Recommend 20 ease visualisation. Default 10. dirs integer vector direction / directions want show/find top genes .","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"findTopGenes — findTopGenes","text":"List containing two slots. First named \"plot\" shows barplot top genes corresponding loadings. Second named \"genes\" vector genes highest (absolute value) loading low-dimensional direction 1. Length parameter highest.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/findTopGenes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"findTopGenes — findTopGenes","text":"","code":"data(MouseData)  wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 8) # create top genes object top_genes_plot = top_genes_obj$plot # select plot top_genes_plot # print plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"generateUmapfromWSIR — generateUmapFromWSIR","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"function generate UMAP coordinates low-dimensional embedding gene expression data. coordinates can later plotted plotUmapFromWSIR function. two functions separate can generate UMAP points using function (may take long time), modify resulting plot much desired plotUmapFromWSIR function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"","code":"generateUmapFromWSIR(WSIR)"},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"WSIR wsir object output wSIR function. wish generate UMAP plots based DR methods, ensure slot named \"scores\" WSIR parameter contains low-dimensional representation exprs.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"matrix UMAP coordinates dimension nrow(coords) * 2. Output function can directly used input plot_umap function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/generateUmapFromWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"generateUmapfromWSIR — generateUmapFromWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 4) # create top genes object umap_plot = plotUmapFromWSIR(umap_coords = umap_coords,   X = sample1_exprs,   highest_genes = top_genes_obj,   n_genes = 4) umap_plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"plotUmapFromWSIR — plotUmapFromWSIR","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"function plot UMAP generated low-dimensional embedding gene expression data. points coloured value genes highest (absolute value) loading selected WSIR direction, default WSIR1.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"","code":"plotUmapFromWSIR(   X,   umap_coords,   highest_genes = NULL,   genes = NULL,   n_genes,   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. umap_coords UMAP coordinates cell output generateUmapFromWSIR function. UMAP coordinates can based dimension reduction method, e.g UMAP coordinates computed WSIR dimension reduction gene expression data, PCs (principal components), low-dimensional matrix. Must matrix dimension nrow(X) * 2. highest_genes output findTopGenes function. Default NULL error message can easily thrown genes highest_genes provided. genes vector gene names (must colnames(X)) wish show UMAP plot. cells plots coloured expression values genes provide . Must provide either genes highest_genes parameter (): provide genes want visualise specific genes, provide highest_genes want visualise genes found important WSIR directions. Default NULL error message can easily thrown genes highest_genes provided. n_genes integer number genes like show. Default number unique genes highest_genes parameter number genes (vector) parameter genes. Use parameter want show important genes (e.g select top 4 n_genes = 4). ... additional parameters ggplot functions, e.g size (size points).","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"Grid umap plots n_genes number plots. shows cells UMAP generated low-dimensional gene expression data, coloured value genes found top_genes.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/plotUmapFromWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotUmapFromWSIR — plotUmapFromWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object umap_coords = generateUmapFromWSIR(WSIR = wsir_obj) top_genes_obj = findTopGenes(WSIR = wsir_obj, highest = 4) # create top genes object umap_plot = plotUmapFromWSIR(umap_coords = umap_coords,   X = sample1_exprs,   highest_genes = top_genes_obj,   n_genes = 4) umap_plot"},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"projectWSIR — projectWSIR","title":"projectWSIR — projectWSIR","text":"function project new gene expression data low-dimensional space","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"projectWSIR — projectWSIR","text":"","code":"projectWSIR(wsir, newdata)"},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"projectWSIR — projectWSIR","text":"wsir wsir object usually output wSIR function. want project new data low-dim space following different DR method, param wsir use list matrix loadings slot 2 (e.g PCA loadings) dimension p * d newdata matrix new gene expression data project low-dimensional space. Must p columns columns X argument used generate wsir.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"projectWSIR — projectWSIR","text":"matrix low-dimensional representation newdata gene expression data","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/projectWSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"projectWSIR — projectWSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) sample2_low_dim_exprs = projectWSIR(wsir = wsir_obj, newdata = sample2_exprs)"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":null,"dir":"Reference","previous_headings":"","what":"sirCategorical — sirCategorical","title":"sirCategorical — sirCategorical","text":"function performs WSIR based provided gene expression data, tile allocations, weight matrix.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sirCategorical — sirCategorical","text":"","code":"sirCategorical(X, Y, maxDirections = 50, W = NULL, ...)"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sirCategorical — sirCategorical","text":"X matrix normalised gene expression data n genes across p cells. Y dataframe 1 column named \"coordinate\" tile allocation cell. slices^2 unique tile IDs column. maxDirections Integer specify maximum number directions retain thee low-dimensional embedding data. Use need certain number downstream task. W Weight matrix created createWeightMatrix. Entry (,j) represents spatial correlation level tiles j. diagonal values 1. provided, SIR implementation used.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirCategorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sirCategorical — sirCategorical","text":"list outputs 5 named slots. output wSIR function: final step wSIR function.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":null,"dir":"Reference","previous_headings":"","what":"sirPCA — sirPCA","title":"sirPCA — sirPCA","text":"function performs eigendecomposition (X^H)^t %*% W %*% (X^H), X^H matrix scaled slice means.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sirPCA — sirPCA","text":"","code":"sirPCA(   sliced_data,   maxDirections = 50,   W = diag(nrow(sliced_data)),   varThreshold = 0.99 )"},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sirPCA — sirPCA","text":"sliced_data matrix scaled slice means maxDirections integer (default 50), number directions want final low-dimensional Z. W matrix slice weights. Output createWeightMatrix function. varThreshold numeric proportion eigenvalues variance t(X_H) %*% W %*% X_H retain. Default 0.99. Select higher threshold include dimensions, lower threshold include less dimensions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/sirPCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sirPCA — sirPCA","text":"list containing: 1) \"eigenvectors\" matrix eigenvectors (X^H)^t %*% W %*% (X^H) 2) \"d\" integer, selected number dimensions 3) \"evalues\" vector eigenvalues (X^H)^t %*% W %*% (X^H)","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":null,"dir":"Reference","previous_headings":"","what":"slicerCategorical — slicerCategorical","title":"slicerCategorical — slicerCategorical","text":"function averages columns X matrix category single-column Y dataframe. used find average position within tile create weight matrix, well create tile means used eigendecomposition step SIR/wSIR.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"slicerCategorical — slicerCategorical","text":"","code":"slicerCategorical(X, Y)"},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"slicerCategorical — slicerCategorical","text":"X matrix dataframe containing columns average Y dataframe single column named 'coordinate'. column contains categorical variable tile ID cell.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/slicerCategorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"slicerCategorical — slicerCategorical","text":"dataframe containing averages column within category Y. Dimension h * p, h number categories Y p number columns X.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":null,"dir":"Reference","previous_headings":"","what":"spatialAllocator — spatialAllocator","title":"spatialAllocator — spatialAllocator","text":"function allocates cell tile based specified number tiles.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"spatialAllocator — spatialAllocator","text":"","code":"spatialAllocator(coords, slices = 3)"},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"spatialAllocator — spatialAllocator","text":"coords dataframe contains spatial position cell. Column names c(\"x\", \"y'). Must include row names integer 1 nrow(coords). automatically included wSIR function prior function. slices integer number slices along two spatial axes. number tiles slices^2 since two spatial axes. E.g set slices = 4 use 4^2 = 16 tiles. Default value 3.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/spatialAllocator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"spatialAllocator — spatialAllocator","text":"output matrix containing slice belonging axis long format. used lapply(coords_split, spatialAllocator, slices) wSIR output dataframe cell's tile allocation \"coordinate\" column.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":null,"dir":"Reference","previous_headings":"","what":"visualiseWSIRDirections — visualiseWSIRDirections","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"function easily visualise low-dimensional gene expression data. function plots cell true spatial coordinates, coloured values WSIR1 / WSIR2 / ... . plots give intuition biological signals contained WSIR directions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"","code":"visualiseWSIRDirections(   coords,   WSIR,   dirs = 6,   mincol = \"blue\",   maxcol = \"red\" )"},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). WSIR wsir object output wSIR function. analyse different DR method, ensure slot named 'directions' contains loadings matrix gene names rownames. Must used coords parameter coords parameter function. dirs integer many low-dimensional directions like visualise. Recommend 10 ease visualisation. Default 6. mincol String colour low values low-dimensional directions. Personal choice user, default \"blue\". maxcol String colour high values low-dimensional directions. Personal choice user, default \"red\".","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"Grid plots dirs number plots. shows cells spatial positions coloured value first 'dirs' WSIR directions.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/visualiseWSIRDirections.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"visualiseWSIRDirections — visualiseWSIRDirections","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = FALSE,   alpha = 4,   slices = 6) # create wsir object vis_obj = visualiseWSIRDirections(coords = sample1_coords, WSIR = wsir_obj, dirs = 8) # create visualisations vis_obj"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIR — wSIR","title":"wSIR — wSIR","text":"function perform supervised dimension reduction gene expression matrix using coordinates dataframe response value. Incorporates weighting mechanism SIR algorithm generate low-dimensional representation data allow projection new single-cell gene expression data low-dimensional space.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIR — wSIR","text":"","code":"wSIR(   X,   coords,   optim_params = FALSE,   alpha_vals = c(0, 1, 2, 4, 8, 12),   slice_vals = c(3, 5, 7, 10, 15, 20),   metric = \"DC\",   nrep = 5,   verbose = FALSE,   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIR — wSIR","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). optim_params logical default FALSE. like wSIR automatically optimise parameters slices alpha based either distance correlation correlation distances evaluation metric. downstream task quite different either metrics, suggest tune two parameters using specific task evaluation metric. case, determine optimal slices alpha values use relevant function, setting optim_params = FALSE. alpha_vals optim_params = TRUE, values alpha optimise wSIR. 0 gives Sliced Inverse Regression (SIR) implementation, larger values represent stronger spatial correlation. Suggest use integers interpretability, can use non-integers. Values must non-negative. slice_vals optim_params = TRUE, values slices optimise wSIR. Suggest maximum value vector around \\(\\sqrt{n/20}\\), upper bound ensures average least 10 cells per tile training set. metric optim_params = TRUE, evaluation metric use parameter tuning. String, either \"DC\" use distance correlation \"CD\" use correlation distances. Default \"DC\". nrep optim_params = TRUE, integer number train/test splits data perform optimisation parameters slices alpha. verbose logical (default FALSE) whether progress messages printed ... arguments passing exploreWSIRParams wSIRSpecifiedParams","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIR — wSIR","text":"wSIR object list containing 5 (named) positions. 1) scores matrix containing low-dimensional representation X wSIR algorithm. Dimension n * d, d chosen include least varThreshold proportion variance. 2) directions matrix containing wSIR directions, dimension p * d. Use project new data low-dimensional space via X_new %*% directions. 3) estd integer stating many dimensions computed low-dimensional space needed account varThreshold proportion variance. number dimensions scores. 4) W matrix weight matrix cells_weight_matrix2 function 5) evalues vector containing p eigenvalues t(X_H) %*% W %*% X_H. varThreshold parameter works evalues, e.g first j directions included sum first j evalues equals 0.95% sum evalues.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wSIR — wSIR","text":"","code":"data(MouseData) wsir_obj = wSIR(X = sample1_exprs,   coords = sample1_coords,   optim_params = TRUE,   alpha_vals = c(0,2,4),   slice_vals = c(3,6,10),   metric = \"DC\",   nrep = 1) # create wsir object #> Time difference of 0.1707692 secs #> Time difference of 0.3771851 secs #> Time difference of 0.1902878 secs #> Time difference of 0.1471441 secs #> Time difference of 0.1736453 secs #> Time difference of 0.1933596 secs #> Time difference of 0.1468866 secs #> Time difference of 0.3150549 secs #> Time difference of 0.1909339 secs"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIROptimisation — wSIROptimisation","title":"wSIROptimisation — wSIROptimisation","text":"function used calculate validation metric best tuning parameters selected","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIROptimisation — wSIROptimisation","text":"","code":"wSIROptimisation(   exprs_train,   coords_train,   exprs_test,   coords_test,   samples_train,   slices,   alpha,   maxDirections,   evalmetrics = c(\"CD\", \"DC\", \"ncol\"),   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIROptimisation — wSIROptimisation","text":"exprs_train matrix normalised gene expression training data. coords_train dataframe containing spatial positions cells 2D space  validation data. Dimension n * 2. Column names must c(\"x\", \"y\"). exprs_test matrix normalised gene expression validation data. samples_train sample ID cell training data. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . slices integer number slices axis tissue. example, slices = 4 creates 4 slices along spatial axis, yielding 4^2 = 16 tiles. Default 8, suggested minimum 3. Suggest tune parameter using exploreWSIRParams() function. alpha integer specify desired strength spatial correlation. alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations. Default value 4, weight matrix value pair tiles inversely proportional physical distance . Suggest tune parameter using exploreWSIRParams() function. maxDirections integer maximum number directions include low-dimenensional embedding. Default 50. evalmetrics evaluation metrics use parameter tuning. String, options : \"DC\" use distance correlation; \"CD\" use correlation distances; \"ncol\" use number columns low-dimensional embedding. Default three, specified metrics = c(\"DC\", \"CD\", \"ncol\"). ... arguments passed wSIRSpecifiedParams","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIROptimisation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIROptimisation — wSIROptimisation","text":"Average metric value selected metric(s) train/test split.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":null,"dir":"Reference","previous_headings":"","what":"wSIR_specified_params — wSIRSpecifiedParams","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"wSIR function specified parameters alpha, slices. ued internally wSIROptimisation wSIR functions, specific values alpha slices.","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"","code":"wSIRSpecifiedParams(   X,   coords,   group = NULL,   samples = rep(1, nrow(coords)),   slices = 8,   alpha = 4,   ... )"},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"X matrix containing normalised gene expression data including n cells p genes, dimension n * p. coords dataframe containing spatial positions n cells 2D space. Dimension n * 2. Column names must c(\"x\", \"y\"). group factor indicating group level information cells within across samples (e.g. cell type). samples sample ID cell. total, must length equal number cells. example, dataset 10000 cells, first 5000 sample 1 remaining 5000 sample 2, write samples = c(rep(1, 5000), rep(2, 5000)) specify first 5000 cells sample 1 remaining sample 2. Default cells sample 1. Sample IDs can format: previous example, write samples = c(rep(\"sample 1\", 5000), rep(\"sample 2\", 5000)), result . slices integer number slices axis tissue. example, slices = 4 creates 4 slices along spatial axis, yielding 4^2 = 16 tiles. Default 8, suggested minimum 3. Suggest tune parameter. alpha integer specify desired strength spatial correlation. Suggest tune parameter testing dataset among e.g values c(0,2,4,8). alpha = 0 gives SIR implementation. Larger values give stronger spatial correlations. ... arguments passed sirCategorical","code":""},{"path":"https://sydneybiox.github.io/wSIR/reference/wSIRSpecifiedParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wSIR_specified_params — wSIRSpecifiedParams","text":"wSIR object list containing 5 (named) positions. 1) scores matrix containing low-dimensional representation X wSIR algorithm. Dimension n * d, d chosen include least varThreshold proportion variance. 2) directions matrix containing WSIR directions, dimension p * d. Use project new data low-dimensional space via X_new %% directions. 3) estd integer stating many dimensions computed low-dimensional space needed account varThreshold proportion variance. number dimensions scores. 4) W matrix weight matrix createWeightMatrix function 5) evalues vector containing p eigenvalues t(X_H) %% W %*% X_H. varThreshold parameter works evalues, e.g first j directions included sum first j evalues equals 0.95% sum evalues.","code":""}]