cellink.tl.external.make_annot_from_donor_data

cellink.tl.external.make_annot_from_donor_data(dd, annot_file, gene_set_file=None, bed_file=None, nomerge=False, run=True, runner=None, scores=None, score_agg='max', gene_coord_file=None, windowsize=100000, out_prefix='ldsc_annot', cleanup_files=True, plink_export_kwargs=None, **kwargs)

Create a binary or continuous S-LDSC annotation file from a DonorData object.

Exports genotype data to PLINK format, then delegates to make_annot_from_bimfile(). Pass scores for continuous mode or gene_set_file / bed_file for binary mode.

Parameters:

• dd (DonorData) – DonorData object containing genotype information.

• annot_file (str) – Output annotation file. Should end in .annot.gz.

• gene_set_file (str, optional) – Binary mode. File of gene names, one per line.

• bed_file (str, optional) – Binary mode. UCSC BED file defining annotation regions.

• nomerge (bool, default False) – Binary mode. Count overlapping BED intervals instead of merging.

• run (bool, default True) – Binary mode. If False, return the command without executing.

• runner (LDSCRunner, optional) – Binary mode. LDSC runner.

• scores (pd.Series, optional) – Continuous mode. Per-gene scores indexed by gene IDs matching gene_coord_file.

• score_agg ({"max", "sum", "mean"}, default "max") – Continuous mode. Aggregation rule for overlapping gene windows.

• gene_coord_file (str, optional) – Gene coordinate file. Required in continuous mode.

• windowsize (int, default 100_000) – Flanking window in bp around each gene body.

• out_prefix (str, default "ldsc_annot") – Prefix for temporary PLINK files created during export.

• cleanup_files (bool, default True) – Remove temporary .bed/.bim/.fam files after writing.

• plink_export_kwargs (dict, optional) – Extra keyword arguments forwarded to to_plink().

• **kwargs – Binary mode. Extra flags forwarded to make_annot.py.

Return type:

dict[str, Any]

Returns:

dict Same as make_annot_from_bimfile().

Examples

Binary annotation:

>>> make_annot_from_donor_data(
...     dd=my_donor_data,
...     annot_file="CD8_Naive.annot.gz",
...     gene_set_file="CD8_Naive.GeneSet",
...     gene_coord_file="gene_coords.txt",
... )

Continuous annotation:

>>> make_annot_from_donor_data(
...     dd=my_donor_data,
...     annot_file="CD8_Naive.annot.gz",
...     scores=specificity_df["CD8 Naive"],
...     gene_coord_file="gene_coords.txt",
... )