Multiple Comparison Correction

Multiple comparison correction for INTENSE analysis.

Provides p-value threshold calculation for family-wise error rate (FWER) and false discovery rate (FDR) control methods.

driada.intense.correction.get_multicomp_correction_thr(fwer, mode='holm', **multicomp_kwargs)[source]

Calculate p-value threshold for multiple hypothesis correction.

Parameters:

fwer (float) – Family-wise error rate or false discovery rate (e.g., 0.05). Must be between 0 and 1.
mode (str or None, optional) – Multiple comparison correction method. Default: ‘holm’. - None: No correction, threshold = fwer - ‘bonferroni’: Bonferroni correction (FWER control) - ‘holm’: Holm-Bonferroni correction (FWER control, more powerful) - ‘fdr_bh’: Benjamini-Hochberg FDR correction
**multicomp_kwargs – Additional arguments for correction method: - For ‘bonferroni’: nhyp (int) - number of hypotheses, must be > 0 - For ‘holm’: all_pvals (list) - all p-values to be tested - For ‘fdr_bh’: all_pvals (list) - all p-values to be tested

Returns:

threshold – Adjusted p-value threshold for individual hypothesis testing. Returns 0 if no p-values pass the correction criteria (reject all).

Return type:

float

Raises:

ValueError – If fwer is not between 0 and 1. If required arguments are missing or invalid. If unknown method specified. If nhyp <= 0 for bonferroni method. If all_pvals is empty for holm or fdr_bh methods.

Notes

FWER methods (bonferroni, holm) control probability of ANY false positive
FDR methods control expected proportion of false positives among rejections
Holm is uniformly more powerful than Bonferroni
FDR typically allows more discoveries but with controlled false positive rate
If no p-values satisfy the correction criteria, threshold is set to 0 (reject all hypotheses)

Examples

>>> # Compare different multiple comparison correction methods
>>> pvals = [0.001, 0.01, 0.02, 0.03, 0.04]
>>>
>>> # No correction - uses raw threshold
>>> thr_none = get_multicomp_correction_thr(0.05, mode=None)
>>> thr_none
0.05
>>>
>>> # Bonferroni correction - most conservative
>>> thr_bonf = get_multicomp_correction_thr(0.05, mode='bonferroni', nhyp=5)
>>> thr_bonf
0.01
>>>
>>> # Holm correction - less conservative than Bonferroni
>>> thr_holm = get_multicomp_correction_thr(0.05, mode='holm', all_pvals=pvals)
>>> round(thr_holm, 4)
0.0125
>>>
>>> # FDR correction - controls false discovery rate
>>> thr_fdr = get_multicomp_correction_thr(0.05, mode='fdr_bh', all_pvals=pvals)
>>> thr_fdr
0.04
>>>
>>> # FDR is least conservative: bonf < holm < fdr < none
>>> thr_bonf < thr_holm < thr_fdr < thr_none
True

P-value threshold calculation for multiple hypothesis testing.

Functions