calour.experiment.Experiment.enrichment¶

Experiment.enrichment(features, dbname, *args, **kwargs)[source]¶

Get the list of enriched annotation terms in features compared to all other features in exp.

Uses the database specific enrichment analysis method.

Parameters:

features (list of str) – The features to test for enrichment (compared to all other features in exp) dbname (str) – the database to use for the annotation terms and enrichment analysis *args (tuple) – **kwargs (dict) – Additional database specific parameters (see per-database module documentation for .enrichment() method)

Returns:

pandas.DataFrame with info about significantly enriched terms. The columns include – ‘feature’ : the feature ID (str) ’pval’ : the p-value for the enrichment (float) ’odif’ : the effect size (float) ’observed’ : the number of observations of this term in group1 (int) ’expected’ : the expected (based on all features) number of observations of this term in group1 (float) ’frac_group1’ : fraction of total terms in group 1 which are the specific term (float) ’frac_group2’ : fraction of total terms in group 2 which are the specific term (float) ’num_group1’ : number of total terms in group 1 which are the specific term (float) ’num_group2’ : number of total terms in group 2 which are the specific term (float) ’description’ : the term (str) numpy.Array where rows are features (ordered like the dataframe), columns are terms, and value is score – for term in feature pandas.DataFrame with info about the features used. columns – ‘group’ : int, the group (1/2) to which the feature belongs ’sequence’: str