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indexing](images/indexing.png){#fig-indexing} + ## Dots in variable names In R it is very common to use dots in symbols and variable names. This is invalid in Python: dots are used for function calls. -When using rpy2, these dots are usually translated to underscores `_`. If this does not happen automatically, you can specify mappings for these symbols. +When using rpy2, these dots are usually translated to underscores `_`. If this automatic translation can result in errors, this does not happen automatically. In this case, you can specify mappings for these symbols. ```{python rpy2_mapping} from rpy2.robjects.packages import importr @@ -36,7 +38,7 @@ tools = importr('tools', robject_translations = d) ## Integers and floating point numbers Unless you explicitely specify, any number is represented as a floating point number in R. By adding a `L` at the end of the number, you specify that it is an integer. -Python can be more strict about using integers or floating point numbers than R. +Python is usually more strict about using integers or floating point numbers than R. ```{r int_example} float_ex <- 12 @@ -45,3 +47,22 @@ int_ex <- 12L is.integer(float_ex) is.integer(int_ex) ``` + +This can often lead to errors when using `reticulate`! If you're calling a Python function and provide it with just a number in R, it probably won't be recognised as an integer, leading to weird errors: + +```{r float_integer_error} +library(reticulate) +bi <- reticulate::import_builtins() + +bi$list(bi$range(0, 5)) +``` + +As you can see, you get errors: `TypeError: 'float' object cannot be interpreted as an integer`. + +This is easily fixed by specifiying integers: + +```{r float_integer_right} +bi$list(bi$range(0L, 5L)) +``` + + diff --git a/book/in_memory/reticulate.qmd b/book/in_memory/reticulate.qmd index 4499f8e..c973ab7 100644 --- a/book/in_memory/reticulate.qmd +++ b/book/in_memory/reticulate.qmd @@ -65,9 +65,11 @@ Jupyter notebooks (and some other notebooks) make this possible from the Python %load_ext rpy2.ipython # line magic that loads the rpy2 ipython extension. # this extension allows the use of the following cell magic -%%R -i input -o output # this line allows to specify inputs (which will be converted to R objects) and outputs (which will be converted back to Python objects) +%%R -i input -o output # this line allows to specify inputs + # (which will be converted to R objects) and outputs + # (which will be converted back to Python objects) # this line is put at the start of a cell - # the rest of the cell will be able to be ran as R code + # the rest of the cell will be run as R code ``` @@ -100,90 +102,11 @@ sc$tl$umap(adata) adata ``` -```{r scanpy_plot} -sc$pl$umap(adata) -``` - - -## Usecase: theoretical reticulate example - -```{r read_in} -library(anndata) - -adata_path <- "../usecase/data/sc_counts_subset.h5ad" -adata <- anndata::read_h5ad(adata_path) -``` - -Subset to a single small molecule and control for computational efficiency: - -```{r select_sm_celltype} -library(dplyr) - -sm_name <- "Belinostat" -control_name <- "Dimethyl Sulfoxide" - -# subset obs -adata <- adata[adata$obs$sm_name %in% c(control_name, sm_name), adata$var$highly_variable] -``` - -```{r import_pandas} -library(reticulate) -pd <- import("pandas", convert = FALSE) - -counts <- as.matrix(adata$X) -``` - -Combine data in a single data frame and compute pseudobulk - -This is a literal translation of the Python code. It is however absolute madness to construct a pandas dataframe in R instead of using just an R dataframe. We basically just needed a matrix with rownames and columnames. - -We provide the rest of the code as a theoretical example, but please reflect if you want to try something similar. - -We will however showcase a useful application of reticulate. - -```{r compute_pseudobulk, eval = FALSE} - -combined <- pd$DataFrame( - counts, - index = adata$obs["plate_well_celltype_reannotated"], - columns = adata$var_names -) -cr <- py_to_r(combined) - -# we lost the rownames -rownames(cr) <- adata$obs_names -cr["celltype"] <- adata$obs["plate_well_celltype_reannotated"] - -pb_X <- group_by(cr, celltype) %>% summarise(across(where(is.numeric), sum)) -``` - -```{r pb_obs_r, eval = FALSE} -pb_obs <- adata$obs[c("sm_name", "cell_type", "plate_name", "well", "plate_well_celltype_reannotated")] -pb_obs <- pb_obs[!duplicated(pb_obs), ] -``` - -```{python pb_obs_py, eval = FALSE} -pb_obs = adata.obs[["sm_name", "cell_type", "plate_name", "well"]].copy() -pb_obs.index = adata.obs["plate_well_celltype_reannotated"] -pb_obs = pb_obs.drop_duplicates() -``` - -```{r pb_anndata, eval = FALSE} -select_X <- pb_X[pb_X$celltype %in% pb_obs$plate_well_celltype_reannotated, ] -select_X <- select_X %>% select(-"celltype") - -pb_adata <- anndata::AnnData( - X = select_X, - obs = pb_obs, - var = adata$var -) -``` - - -```{r store_pseudobulk, eval=FALSE} -write_h5ad(pb_adata, "../usecase/data/pseudobulk.h5ad") +We can't easily show the result of the plot in this Quarto notebook, but we can save it and show it: +```{r scanpy_plot} +path <- "umap.png" +sc$pl$umap(adata, color="leiden_res1", save=path) ``` -## Usecase: useful reticulate example - +![UMAP plot of the adata object](../../figuresumapumap.png){#fig-umap.png}) \ No newline at end of file diff --git a/figures/umapumap.png b/figures/umapumap.png new file mode 100644 index 0000000000000000000000000000000000000000..f2b2bcd9465e063fbccaa2a110afb6eb472dfe24 GIT binary patch literal 218009 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osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda - sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09 - md5: d96942c06c3e84bfcc5efb038724a7fd - depends: - - __osx >=11.0 - - libzlib >=1.2.13,<2.0.0a0 - license: BSD-3-Clause - license_family: BSD - purls: [] - size: 405089 - timestamp: 1714723101397 diff --git a/scripts/1_load_data.sh b/scripts/1_load_data.sh index 5d5fd42..042ede1 100644 --- a/scripts/1_load_data.sh +++ b/scripts/1_load_data.sh @@ -1,6 +1,6 @@ -if [[ ! -f usecase/data/sc_counts_reannotated_with_counts.h5ad ]]; then +if [[ ! -f book/usecase/data/sc_counts_reannotated_with_counts.h5ad ]]; then aws s3 cp \ --no-sign-request \ s3://openproblems-bio/public/neurips-2023-competition/sc_counts_reannotated_with_counts.h5ad \ - usecase/data/sc_counts_reannotated_with_counts.h5ad + book/usecase/data/sc_counts_reannotated_with_counts.h5ad fi