troutpy.tl.quantify_overexpression

troutpy.tl.quantify_overexpression#

troutpy.tl.quantify_overexpression(sdata, codeword_key, control_codewords, gene_key='gene', layer='transcripts', copy=False)#

Quantify gene overexpression relative to a Poisson noise model derived from control codewords.

For each gene, computes the observed count, log fold-change over the noise baseline (mean control count), a one-sided Poisson survival-function p-value, and Benjamini–Hochberg FDR correction. Results are stored in sdata["xrna_metadata"].var.

Parameters:

  • sdata (spatialdata.SpatialData) – SpatialData object containing a points layer with transcripts and an "xrna_metadata" table (created if absent).

  • codeword_key (str) – Column in the transcript points layer that holds the codeword category used to identify control probes.

  • control_codewords (list of str) – Codeword category values that identify control (noise) probes.

  • gene_key (str, optional) – Column in the transcript points layer containing gene identifiers. Defaults to "gene".

  • layer (str, optional) – Key in sdata.points holding the transcript data. Defaults to "transcripts".

  • copy (bool, optional) – If True, return the updated SpatialData object; otherwise modify in place and return None. Defaults to False.

Return type:

SpatialData

Returns:

spatialdata.SpatialData or None Updated SpatialData with columns "count", "logfoldchange_over_noise", "p_val_noise", "is_control", and "fdr_noise" added to sdata["xrna_metadata"].var if copy=True; otherwise None.

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