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, chrom_sizes, *)	Import data fragment files and compute basic QC metrics.

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 by selecting the most accessibile features across all cells unless max_iter > 1.
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 according to the doublet probability or doublet score.

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.
pp.scanorama_integrate(adata, *, batch[, ...])	Use Scanorama [Hie19] to integrate different experiments.