snapatac2.metrics.tsse#

snapatac2.metrics.tsse(adata, gene_anno, *, exclude_chroms=['chrM', 'M'], inplace=True, n_jobs=8)[source]#

Compute the TSS enrichment score (TSSe) for each cell.

import_data must be ran first in order to use this function.

Parameters:

  • adata (AnnData | list[AnnData]) – The (annotated) data matrix of shape n_obs x n_vars. Rows correspond to cells and columns to regions. adata could also be a list of AnnData objects. In this case, the function will be applied to each AnnData object in parallel.

  • gene_anno (Genome | Path) – A Genome object or a GTF/GFF file containing the gene annotation.

  • exclude_chroms (list[str] | str | None) – A list of chromosomes to exclude.

  • inplace (bool) – Whether to add the results to adata.obs or return it as a dictionary.

  • n_jobs (int) – Number of jobs to run in parallel when adata is a list. If n_jobs=-1, all CPUs will be used.

Returns:

If inplace = True, cell-level TSSe scores are computed and stored in adata.obs['tsse']. Library-level TSSe scores are stored in adata.uns['library_tsse']. Fraction of fragments overlapping TSS are stored in adata.uns['frac_overlap_TSS']. If inplace = False, return a tuple containing all these values.

Return type:

tuple[np.ndarray, tuple[float, float]] | list[tuple[np.ndarray, tuple[float, float]]] | None

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.metrics.tsse(data, snap.genome.hg38)
>>> print(data.obs['tsse'].head())
AAACTGCAGACTCGGA-1    32.129514
AAAGATGCACCTATTT-1    22.052786
AAAGATGCAGATACAA-1    27.109808
AAAGGGCTCGCTCTAC-1    24.990329
AAATGAGAGTCCCGCA-1    33.264463
Name: tsse, dtype: float64
On this page