Usage Principles

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-enabled cupy arrays and sparse matrices. Rapids-singlecell leverages this capability to perform analyses directly on AnnData objects with cupy arrays.

To get your AnnData object onto the GPU via cupy, you can move 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 mlm(), ulm() and aucell() like:

import decoupler as dc

model = dc.op.resource("PanglaoDB", organism="human")
rsc.dcg.ulm(adata, model , tmin=3)
acts_mlm = dc.pp.get_obsm(adata, key="score_ulm")
sc.pl.umap(acts_mlm, color=['NK cells'], cmap='coolwarm', vcenter=0)

Pertpy-compatible API (use `rsc.ptg`)#

rapids_singlecell exposes a pertpy-compatible, GPU-accelerated API under rapids_singlecell.ptg like:

from rapids_singlecell import ptg

distance = ptg.Distance(metric="edistance", obsm_key="X_pca")
result = distance.pairwise(adata, groupby="perturbation")
res, res_var = distance.pairwise(
	adata, groupby="perturbation", bootstrap=True, n_bootstrap=100, multi_gpu=None
)

Squidpy GPU helpers (use `rsc.squidpy_gpu`)#

rapids_singlecell includes GPU-accelerated implementations of common squidpy workflows under rapids_singlecell.gr like:

from rapids_singlecell import squidpy_gpu as sqg

sqg.spatial_autocorr(
	adata,
	connectivity_key="spatial_connectivities",
	mode="moran",
	n_perms=500,
)
sqg.co_occurrence(adata, cluster_key="labels", interval=50)
sqg.ligrec(adata, cluster_key="labels", n_perms=1000)