Align pow as method

NAMESPACE

@@ -24,6 +24,7 @@ export(alignDt)
 export(alignPlots)
 export(alignRt)
 export(align_ip)
+export(align_pow)
 export(baseline)
 export(clusterPeaks)
 export(create_annotations_table)
@@ -72,6 +73,7 @@ exportMethods(align)
 exportMethods(alignDt)
 exportMethods(alignRt)
 exportMethods(align_ip)
+exportMethods(align_pow)
 exportMethods(baseline)
 exportMethods(decimate)
 exportMethods(description)

R/aaa-AllGenerics.R

@@ -106,14 +106,22 @@ setGeneric("decimate", function(object, ...) standardGeneric("decimate"))
 #' @export
 setGeneric("align", function(object, ...) standardGeneric("align"))
 
-#' @describeIn GCIMS-generics Align to injection point an object
+#' @describeIn GCIMS-generics Align to injection point in retention time
 #'
 #' @param object An object to align
 #' @param ... Further arguments, possibly used by downstream methods.
 #' @return The object, modified
 #' @export
 setGeneric("align_ip", function(object, ...) standardGeneric("align_ip"))
 
+#' @describeIn GCIMS-generics Align in retention time with POW algorithm
+#'
+#' @param object An object to align
+#' @param ... Further arguments, possibly used by downstream methods.
+#' @return The object, modified
+#' @export
+setGeneric("align_pow", function(object, ...) standardGeneric("align_pow"))
+
 #' @describeIn GCIMS-generics Align an object in drift time
 #'
 #' @param x An object to align

R/align_ip_GCIMSSample.R

@@ -2,10 +2,10 @@
 #'
 #' Aligns a sample to the reference injection point
 #'
-#' @param object A [GCIMSDataset] object, modified in-place
+#' @param object A [GCIMSSample] object, modified in-place
 #' @param min_start The points that will be used before the injection point
 #' @param rt_ref Retention time vector to be used
-#' @return The modified [GCIMSDataset]
+#' @return The modified [GCIMSSample]
 #' @export
 methods::setMethod(
   "align_ip",

R/align_pow-GCIMSDataset.R

@@ -1,32 +1,47 @@
-align_pow <- function(x,
-                      lambdas = pracma::logspace(-2, 4, 31),
-                      p = 10,
-                      max_it = 1000){
-  rics <- x$getRIC()
-  idx_y <- Align::select_reference(rics)
-  X <- as.matrix(rics[-idx_y,])
-  y <- as.matrix(rics[idx_y,])
-  n_samples <- nrow(X)
-  m <- length(y)
-  v <- rep(1,m)
-  iv <- seq(2, m - 1, by = p)
-  v[iv] <- 0
-  W <- Matrix::Diagonal(x = v)
-  val<-Align::compute_val_error(X,y,W,iv,lambdas)
-  opar <- Align::optimize_params(n_samples, lambdas, val$ti_ix, val$e_ix)
-  best_lambdas2 <- opar$best_params
+#' Align a [GCIMSDataset] using the POW algorithm
+#'
+#' Uses the POW algorithm to generate waprs and align the samples
+#'
+#' @param object A [GCIMSDataset] object, modified in-place
+#' @param lambdas vector of lambdas to be tested for the POW algorithm
+#' @param p points to be taken for validation for the POW algorithm
+#' @param max_it maximum iterations for applying the POW algorithm
+#' @return The modified [GCIMSDataset]
+#' @export
 
-  w<-mapply(Align::pow,x=as.list(data.frame(t(X))), lambda2=as.list(best_lambdas2),MoreArgs = list(y=y,max_it = max_it))
-  w<-as.list((as.data.frame(w)))
-  w<-append(w,list(1:m),idx_y-1)
-  names(w)<-x$sampleNames
+methods::setMethod(
+  "align_pow",
+  "GCIMSDataset",
+  function(object,
+           lambdas = pracma::logspace(-2, 4, 31),
+           p = 10,
+           max_it = 1000){
+    rics <- object$getRIC()
+    idx_y <- Align::select_reference(rics)
+    X <- as.matrix(rics[-idx_y,])
+    y <- as.matrix(rics[idx_y,])
+    n_samples <- nrow(X)
+    m <- length(y)
+    v <- rep(1,m)
+    iv <- seq(2, m - 1, by = p)
+    v[iv] <- 0
+    W <- Matrix::Diagonal(x = v)
+    val<-Align::compute_val_error(X,y,W,iv,lambdas)
+    opar <- Align::optimize_params(n_samples, lambdas, val$ti_ix, val$e_ix)
+    best_lambdas2 <- opar$best_params
 
-  delayed_op <- DelayedOperation(
-    name = "align_pow",
-    fun = align_pow_sample,
-    params_iter = list(w=w)
-  )
-  x$appendDelayedOp(delayed_op)
-  x <- GCIMS:::extract_RIC_and_TIS(x)
-  invisible(x)
-}
+    w<-mapply(Align::pow,x=as.list(data.frame(t(X))), lambda2=as.list(best_lambdas2),MoreArgs = list(y=y,max_it = max_it))
+    w<-as.list((as.data.frame(w)))
+    w<-append(w,list(1:m),idx_y-1)
+    names(w)<-object$sampleNames
+
+    delayed_op <- DelayedOperation(
+      name = "align_pow",
+      fun = align_pow,
+      params_iter = list(w=w)
+    )
+    object$appendDelayedOp(delayed_op)
+    object <- GCIMS:::extract_RIC_and_TIS(object)
+    invisible(object)
+  }
+)

R/align_pow-GCIMSSample.R

@@ -1,8 +1,21 @@
-align_pow_sample <- function(x,w){
-  int <- GCIMS::intensity(x)
-  inter <- t(apply(int, 1, Align::interpolation, w = w, return = FALSE))
-  sel <- !is.na(inter[1,])
-  x@retention_time <- x@retention_time[sel]
-  x@data <- inter[,sel]
-  return(x)
-}
+#' Warp a GCIMSSample
+#'
+#' Warps a GCIMSSample with the provided warp
+#'
+#' @param object A [GCIMSSample] object, modified in-place
+#' @param w warp to be applied to the GCIMSSample
+#' @return The modified [GCIMSSample]
+#' @export
+
+methods::setMethod(
+  "align_pow",
+  "GCIMSSample",
+  function(object,w){
+    int <- GCIMS::intensity(object)
+    inter <- t(apply(int, 1, Align::interpolation, w = w, return = FALSE))
+    sel <- !is.na(inter[1,])
+    object@retention_time <- object@retention_time[sel]
+    object@data <- inter[,sel]
+    return(object)
+  }
+)

vignettes/introduction-to-gcims.Rmd

@@ -626,14 +626,14 @@ peak_list <- peaks(dataset)
 
 ```{r}
 peak_table <- peakTable(peak_list, aggregate_conflicting_peaks = max)
-peak_table$peak_table_matrix[,22:27]
+tail(peak_table$peak_table_matrix)
 ```
 We showed the last 6 clusters where we have "NA" Values
 
 # Imputation
 ```{r}
 peak_table_imputed <- imputePeakTable(peak_table$peak_table_matrix, dataset, peak_clustering$cluster_stats)
-peak_table_imputed[,22:27]
+tail(peak_table_imputed)
 ```
 As we can see the "NA" values were imputed