snapatac2.pp.recipe_10x_metrics#

snapatac2.pp.recipe_10x_metrics(bam_file, output_fragment_file, output_h5ad_file, peaks=None, **kwargs)[source]#

Generate 10x-style ATAC QC metrics from a raw BAM file.

Use this recipe to convert a BAM file to fragments, import the fragments into an h5ad file, compute targeting metrics, call peaks when needed, and summarize library-level QC values in one dictionary. Keyword arguments are forwarded to the individual preprocessing and metric functions when their signatures accept those names.

Anti-Patterns#

Do NOT use this recipe when you only need an existing fragment file imported; call snap.pp.import_fragments directly.
Do NOT omit required downstream inputs such as chrom_sizes and gene_anno; they are supplied through **kwargs and used by the called functions.

type bam_file:: Path
param bam_file:: Path to the input BAM file.
type output_fragment_file:: Path
param output_fragment_file:: Path where the generated fragment file is written.
type output_h5ad_file:: Path
param output_h5ad_file:: Path where the intermediate AnnData object is written.
type peaks:: Path | list[str] | None
param peaks:: Path to a BED-like peak file or a list of peak regions formatted as "chrom:start-end". If None, MACS3 is run to call peaks.
type **kwargs:
param **kwargs:: Additional arguments accepted by functions used in the recipe, including make_fragment_file, import_fragments, metrics.tsse, and related calls.
returns:: Nested dictionary containing sequencing, cell, library-complexity, mapping, and targeting QC metrics.
rtype:: dict

Examples

>>> import snapatac2 as snap
>>> bam_file = snap.datasets.pbmc500(type='bam')
>>> metrics = snap.pp.recipe_10x_metrics(
...     bam_file,
...     'fragments.tsv.gz',
...     'data.h5ad',
...     barcode_tag='CB',
...     source='10x',
...     chrom_sizes=snap.genome.hg38,
...     gene_anno=snap.genome.hg38,
... )
>>> sorted(metrics)
['Cells', 'Library Complexity', 'Mapping', 'Sequencing', 'Targeting']