snapatac2.pp.add_tile_matrix#
- snapatac2.pp.add_tile_matrix(adata, *, bin_size=500, inplace=True, chunk_size=500, exclude_chroms=['chrM', 'chrY', 'M', 'Y'], min_frag_size=None, max_frag_size=None, count_frag_as_reads=True, file=None, backend='hdf5', n_jobs=8)[source]#
Generate cell by bin count matrix.
This function is used to generate and add a cell by bin count matrix to the AnnData object.
import_datamust be ran first in order to use this function.- Parameters:
adata (
AnnData|list[AnnData]) – The (annotated) data matrix of shapen_obsxn_vars. Rows correspond to cells and columns to regions.adatacould also be a list of AnnData objects wheninplace=True. In this case, the function will be applied to each AnnData object in parallel.bin_size (
int) – The size of consecutive genomic regions used to record the counts.inplace (
bool) – Whether to add the tile matrix to the AnnData object or return a new AnnData object.chunk_size (
int) – Increasing the chunk_size speeds up I/O but uses more memory.exclude_chroms (
list[str] |str|None) – A list of chromosomes to exclude.min_frag_size (
Optional[int]) – Minimum fragment size to include.max_frag_size (
Optional[int]) – Maximum fragment size to include.count_frag_as_reads (
bool) – Whether to count fragments as reads. IfTrue, each fragment is converted to two points representing both ends of the fragment.file (
Optional[Path]) – File name of the output file used to store the result. If provided, result will be saved to a backed AnnData, otherwise an in-memory AnnData is used. This has no effect wheninplace=True.backend (
Literal['hdf5']) – The backend to use for storing the result. IfNone, the default backend will be used.n_jobs (
int) – Number of jobs to run in parallel whenadatais a list. Ifn_jobs=-1, all CPUs will be used.
- Returns:
An annotated data matrix of shape
n_obsxn_vars. Rows correspond to cells and columns to bins. Iffile=None, an in-memory AnnData will be returned, otherwise a backed AnnData is returned.- Return type:
AnnData | ad.AnnData | None
See also
Examples
>>> import snapatac2 as snap >>> data = snap.pp.import_data(snap.datasets.pbmc500(downsample=True), chrom_sizes=snap.genome.hg38, sorted_by_barcode=False) >>> snap.pp.add_tile_matrix(data, bin_size=500) >>> print(data) AnnData object with n_obs × n_vars = 585 × 6062095 obs: 'n_fragment', 'frac_dup', 'frac_mito' uns: 'reference_sequences' obsm: 'fragment_paired'