EnrichmentResult

The results from enrichR are return in an EnrichmentResult.

class magine.enrichment.enrichment_result.EnrichmentResult(*args, **kwargs)

Bases: magine.data.base.BaseData

all_genes_from_df()

Returns all genes from gene columns in a set

Returns

set

calc_dist(level='datafame')

dist_matrix(figsize=(8, 8), level='dataframe')

Create a distance matrix of all term similarity

Parameters

figsizetuple

Size of figure

levelstr, {‘dataframe’, ‘each’}

How to treats term_name to genes. Dataframe compresses all genes from all sample_ids into same term. ‘each’ treats each term_name individually.

Returns

matplotlib.Figure

filter_based_on_words(words, inplace=False)

Filter term_name based on key terms

Parameters

wordslist, str

List of words to use to keep rows in dataframe

inplacebool

Filter the dataframe in place or return filtered copy

Returns

pandas.DataFrame

filter_multi(p_value=None, combined_score=None, db=None, sample_id=None, category=None, rank=None, inplace=False)

Filters an enrichment array.

This is an aggregate function that allows ones to filter an entire dataframe with a single function call.

Parameters

p_valuefloat

filters all values less than or equal

combined_scorefloat

filters all values greater than or equal

dbstr, list

sample_idstr, list

categorystr, list

rankint

inplacebool

Filter inplace

Returns

new_dataEnrichmentResult

filter_rows(column, options, inplace=False)

Filters a pandas dataframe provides a column and filter selection.

Parameters

columnstr

optionsstr, list

Can be a single entry or a list

inplacebool

Filter inplace

Returns

——-

pd.DataFrame

find_similar_terms(term, level='sample', remove_subset=True)

Calculates similarity of all other terms to given term

Parameters

termstr

levelstr

Sample or dataframe level, flattens all terms to one set of genes

remove_subsetbool

If any term is a subset of the other term, a score of 1 will be used instead of jaccard index.

Returns

pd.DataFrame

remove_redundant(threshold=0.75, verbose=False, level='sample', sort_by='combined_score', inplace=False)

Calculate similarity between all term sets and removes redundant terms.

Parameters

thresholdfloat, default 0.75

verbosebool, default False

Print similarity scores and removed terms.

level{‘sample’, ‘dataframe’}, default ‘sample’

Level to filter dataframe. ‘sample’ will pivot the dataframe and filter each group of ‘sample_id’ individually. ‘dataframe’ will merge all genes that share the same ‘term_name’.

sort_by{‘combined_score’, ‘rank’, ‘adj_p_value’, ‘n_genes’},

default ‘combined_score’ Keyword to sort the dataframe. The scoring starts at the top term and compares to all the lower terms. Options are

inplacebool

Filter the dataframe in place or return filtered copy

Returns

pandas.DataFrame

show_terms_below(term, level='dataframe', threshold=0.7, remove_subset=True)

Find terms that were removed by remove_redundant

Parameters

termstr

levelstr

thresholdfloat

remove_subsetbool

Returns

EnrichmentResult

term_to_genes(term)

Get set of genes of provides term(s)

Parameters

termstr, list

Returns

set

term_to_genes_dict(term_list=None)

Parameters

term_listlist

Returns

OrderedDict

unique_terms(threshold=0.75, verbose=False, level='dataframe')

Parameters

thresholdfloat

verbosebool

levelstr, {‘dataframe’, ‘each’}

This can be saved just like a pandas.DataFrame and loaded in using

magine.enrichment.enrichment_result.load_enrichment_csv(file_name, **args)

Load data into EnrichmentResult data class

Parameters

file_namestr

Returns

EnrichmentResult