Usage Principles

Import

import rapids_singlecell as rsc

Workflow

The workflow of rapids-singlecell is basically the same as scanpy’s. The main difference is the speed at which rsc can analyze the data. For more information please checkout the notebooks and API documentation.

AnnData setup

AnnData supports GPU arrays and Sparse Matrices.

Rapids-singlecell leverages this capability to perform analyses directly on GPU-enabled AnnData objects.

To get your AnnData object onto the GPU you can set X or each layers to a GPU based matrix.

adata.X = cpx.scipy.sparse.csr_matrix(adata.X)  # moves `.X` to the GPU
adata.X = adata.X.get() # moves `.X` back to the CPU

You can also use rapids_singlecell.get to move arrays and matrices.

rsc.get.anndata_to_GPU(adata) # moves `.X` to the GPU
rsc.get.anndata_to_CPU(adata) # moves `.X` to the CPU

Preprocessing

The preprocessing can be handled by the functions in pp. They offer accelerated versions of functions within scanpy.pp.

Example:

rsc.pp.highly_variable_genes(adata, n_top_genes=5000, flavor="seurat_v3", batch_key= "PatientNumber", layer = "counts")
adata = adata[:,adata.var["highly_variable"]==True]
rsc.pp.regress_out(adata,keys=["n_counts", "percent_MT"])
rsc.pp.scale(adata,max_value=10)

Tools

The functions provided in tl are designed to as near drop-in replacements for the functions in scanpy.tl, but offer significantly improved performance. Consequently, you can continue to use scanpy’s plotting API.

Example:

rsc.tl.tsne(adata)
sc.pl.tsne(adata, color="leiden")

Decoupler-GPU

dcg offers accelerated drop in replacements for run_mlm() and run_wsum()

Example:

import decoupler as dc
model = dc.get_progeny(organism='human', top=100)
rsc.dcg.run_mlm(mat=adata, net=net, source='source', target='target', weight='weight', verbose=True)
acts_mlm = dc.get_acts(adata, obsm_key='mlm_estimate')
sc.pl.umap(acts_mlm, color=['KLF5',"FOXA1", 'CellType'], cmap='coolwarm', vcenter=0)