Preprocessing: `pp`#

BAM/Fragment file processing#

`pp.make_fragment_file`(bam_file, output_file)	Convert a BAM file to a fragment file.
`pp.import_data`(fragment_file, *[, file, ...])	Import dataset and compute QC metrics.

Additional Quality Control#

pp.add_frip(adata, regions, *[, inplace, n_jobs])

Add fraction of reads in peaks (FRiP) to the AnnData object.

Matrix operation#

`pp.add_tile_matrix`(adata, *[, bin_size, ...])	Generate cell by bin count matrix.
`pp.make_peak_matrix`(adata, *[, use_rep, ...])	Generate cell by peak count matrix.
`pp.make_gene_matrix`(adata, gene_anno, *[, ...])	Generate cell by gene activity matrix.
`pp.filter_cells`(data[, min_counts, ...])	Filter cell outliers based on counts and numbers of genes expressed.
`pp.select_features`(adata[, n_features, ...])	Perform feature selection.
`pp.knn`(adata[, n_neighbors, use_dims, ...])	Compute a neighborhood graph of observations.

Doublet removal#

`pp.scrublet`(adata[, features, n_comps, ...])	Compute probability of being a doublet using the scrublet algorithm.
`pp.filter_doublets`(adata[, ...])	Remove doublets.

Data Integration#

`pp.mnc_correct`(adata, *, batch[, ...])	A modified MNN-Correct algorithm based on cluster centroid.
`pp.harmony`(adata, *, batch[, use_rep, ...])	Use harmonypy to integrate different experiments.