Demo with scanpy

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

knitr::opts_chunk$set(eval = reticulate::py_module_available("anndata") && reticulate::py_module_available("scanpy"))

We've found that by using anndata for R, interacting with other anndata-based Python packages becomes super easy!

Download and load dataset

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)
sc <- import("scanpy")

url <- "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"
ad <- sc$read_10x_h5("dataset.h5", backup_url = url)

ad

Preprocessing dataset

The resuling dataset is a wrapper for the Python class but behaves very much like an R object:

ad[1:5, 3:5]
dim(ad)

But you can still call scanpy functions on it, for example to perform preprocessing.

sc$pp$filter_cells(ad, min_genes = 200)
sc$pp$filter_genes(ad, min_cells = 3)
sc$pp$normalize_per_cell(ad)
sc$pp$log1p(ad)

Analysing your dataset in R

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, ])


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anndata documentation built on Sept. 10, 2021, 5:07 p.m.