# Demo with 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, ])