We’ve found that by using anndata for R, interacting with other anndata-based Python packages becomes super easy!
Let’s use a 10x dataset from the 10x genomics website. You can download it to an anndata object with scanpy as follows:
library(anndata) library(reticulate) <- import("scanpy") sc <- "https://cf.10xgenomics.com/samples/cell-exp/6.0.0/SC3_v3_NextGem_DI_CellPlex_CSP_DTC_Sorted_30K_Squamous_Cell_Carcinoma/SC3_v3_NextGem_DI_CellPlex_CSP_DTC_Sorted_30K_Squamous_Cell_Carcinoma_count_sample_feature_bc_matrix.h5" url <- sc$read_10x_h5("dataset.h5", backup_url = url) ad ad
The resuling dataset is a wrapper for the Python class but behaves very much like an R object:
1:5, 3:5] ad[dim(ad)
But you can still call scanpy functions on it, for example to perform preprocessing.
$pp$filter_cells(ad, min_genes = 200) sc$pp$filter_genes(ad, min_cells = 3) sc$pp$normalize_per_cell(ad) sc$pp$log1p(ad)sc
You can seamlessly switch back to using your dataset with other R functions, for example by calculating the rowMeans of the expression matrix.
library(Matrix) rowMeans(ad$X[1:10, ])