skbio.stats.composition.ANCOMBCResult.global_test#

ANCOMBCResult.global_test(alpha='inherit', p_adjust='inherit')[source]#

Perform global test for differential abundance across groups.

The global test identifies features that are differentially abundant between at least two groups across three or more groups.

Changed in version 0.7.4: Fixed a bug in the global test, which would produce inaccurate results. Please update the program. The main results are not impacted.

Parameters:
alphafloat or “inherit”, optional

Significance level. Default is “inherit”, which will use the value supplied upstream.

p_adjuststr, optional

Multiple testing correction method. Default is “inherit”, which will use the p-value correction method supplied upstream.

Returns:
pd.DataFrame

Global test result. Columns are:

  • FeatureID: Feature identifier, i.e., dependent variable.

  • W: W-statistic quantifying the overall evidence against null hypothesis (mean abundance of the feature is the same across all groups).

  • pvalue: p-value of the W-statistic.

  • qvalue: Corrected p-value.

  • Signif: Whether at least one group mean is different from others.