gretapy.mt.lit_grn

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gretapy.mt.lit_grn#

gretapy.mt.lit_grn(mdata, grn='CollecTRI', organism='hg38', min_targets=5, verbose=False)#

Generate a GRN from literature-based prior knowledge filtered by available peaks and genes.

This method accepts a reference GRN (CollecTRI, DoRothEA, or a custom DataFrame) and promoter annotations, then prunes the network based on the peaks and genes available in the input MuData object.

Parameters:

  • mdata (MuData) – MuData object with “rna” and “atac” modalities.

  • grn (str | DataFrame (default: 'CollecTRI')) – Which GRN to use. Can be “CollecTRI”, “DoRothEA”, or a pandas DataFrame with columns: source, target, weight. Default is “CollecTRI”.

  • organism (str (default: 'hg38')) – Which organism to use. Default is “hg38”.

  • min_targets (int (default: 5)) – Minimum number of targets required for a TF to be included. Default is 5.

  • verbose (bool (default: False)) – Whether to print progress messages. Default is False.

Return type:

DataFrame

Returns:

pd.DataFrame GRN DataFrame with columns: source, cre, target, score. - source: Transcription factor name. - cre: Cis-regulatory element (peak) in format chrX-start-end. - target: Target gene name. - score: Edge weight from the reference GRN.

Examples

>>> import mudata as mu
>>> import gretapy as gt
>>> mdata = mu.read("my_multiome.h5mu")
>>> grn = gt.mt.lit_grn(mdata, grn="CollecTRI", organism="hg38")
>>> grn.head()
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