campmc.preprocess

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campmc.preprocess#

campmc.preprocess(adata, *, min_genes=200, min_cells=3, n_hvg=2000, n_pcs=50, target_sum=10000.0, copy=False)#

Filter, normalize, select HVGs, and compute PCA.

Expects raw (or non-negative) counts in adata.X. After this call, adata.obsm['X_pca'] is ready for partition().

Parameters:
adata AnnData

Annotated data matrix with count-like values in X.

min_genes int (default: 200)

Minimum genes expressed per cell (passed to scanpy.pp.filter_cells).

min_cells int (default: 3)

Minimum cells expressing a gene (passed to scanpy.pp.filter_genes).

n_hvg int (default: 2000)

Number of highly variable genes to retain.

n_pcs int (default: 50)

Number of principal components to compute.

target_sum float (default: 10000.0)

Target library size for scanpy.pp.normalize_total.

copy bool (default: False)

If True, operate on a copy and return it; otherwise modify adata in place and return None.

Return type:

AnnData | None

Returns:

AnnData | None Updated object if copy=True, else None.

Notes

Negative values in X (e.g. from prior scaling) are clipped to zero before normalization. The object is subset to HVGs and scaled before PCA.

Examples

import campmc as cp
import scanpy as sc
adata = sc.datasets.pbmc3k()
cp.preprocess(adata, n_hvg=500, n_pcs=30)
adata.obsm["X_pca"].shape

(2700, 30)
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