acore.enrichment_analysis package

acore.enrichment_analysis package#

Enrichment Analysis Module. Contains different functions to perform enrichment analysis.

Most things in this module are covered in https://www.youtube.com/watch?v=2NC1QOXmc5o by Lars Juhl Jensen.

run_site_regulation_enrichment(regulation_data: DataFrame, annotation: DataFrame, identifier: str = 'identifier', groups: list[str] = ('group1', 'group2'), annotation_col: str = 'annotation', rejected_col: str = 'rejected', group_col: str = 'group', method: str = 'fisher', regex: str = '(\\w+~.+)_\\w\\d+\\-\\w+', correction: str = 'fdr_bh', remove_duplicates: bool = False) → DataFrame[EnrichmentAnalysisSchema][source]#

This function runs a simple enrichment analysis for significantly regulated protein sites in a dataset.

Parameters:

  • regulation_data – pandas.DataFrame resulting from differential regulation analysis.

  • annotation – pandas.DataFrame with annotations for features (columns: ‘annotation’, ‘identifier’ (feature identifiers), and ‘source’).

  • identifier (str) – name of the column from annotation containing feature identifiers.

  • groups (list) – column names from regulation_data containing group identifiers.

  • annotation_col (str) – name of the column from annotation containing annotation terms.

  • rejected_col (str) – name of the column from regulation_data containing boolean for rejected null hypothesis.

  • group_col (str) – column name for new column in annotation dataframe determining if feature belongs to foreground or background.

  • method (str) – method used to compute enrichment (only ‘fisher’ is supported currently).

  • regex (str) – how to extract the annotated identifier from the site identifier

Returns:

pandas.DataFrame with columns: ‘terms’, ‘identifiers’, ‘foreground’, ‘background’, foreground_pop, background_pop, ‘pvalue’, ‘padj’ and ‘rejected’.

Raises:

ValueError – if regulation_data is None or empty.

Example:

result = run_site_regulation_enrichment(regulation_data,
    annotation,
    identifier='identifier',
    groups=['group1', 'group2'],
    annotation_col='annotation',
    rejected_col='rejected',
    group_col='group',
    method='fisher',
    match="(\\w+~.+)_\\w\\d+\\-\\w+"
)
run_up_down_regulation_enrichment(regulation_data: DataFrame, annotation: DataFrame, identifier: str = 'identifier', groups: list[str] = ('group1', 'group2'), annotation_col: str = 'annotation', pval_col: str = 'pval', group_col: str = 'group', log2fc_col: str = 'log2FC', method: str = 'fisher', min_detected_in_set: int = 2, correction: str = 'fdr_bh', correction_alpha: float = 0.05, lfc_cutoff: float = 1) → DataFrame[EnrichmentAnalysisSchema][source]#

This function runs a simple enrichment analysis for significantly regulated proteins distinguishing between up- and down-regulated.

Parameters:

  • regulation_data (pandas.DataFrame) – pandas.DataFrame resulting from differential regulation analysis (CKG’s regulation table).

  • annotation (pandas.DataFrame) – pandas.DataFrame with annotations for features (columns: ‘annotation’, ‘identifier’ (feature identifiers), and ‘source’).

  • identifier (str) – name of the column from annotation containing feature identifiers.

  • groups (list[str]) –

    column names from regulation_data containing group identifiers. See pandas.DataFrame.groupby for more information.

  • annotation_col (str) – name of the column from annotation containing annotation terms.

  • rejected_col (str) – name of the column from regulation_data containing boolean for rejected null hypothesis.

  • group_col (str) – column name for new column in annotation dataframe determining if feature belongs to foreground or background.

  • method (str) – method used to compute enrichment (only ‘fisher’ is supported currently).

  • correction (str) – method to be used for multiple-testing correction

  • alpha (float) – adjusted p-value cutoff to define significance

  • lfc_cutoff (float) – log fold-change cutoff to define practical significance

Returns:

DataFrame adhering to EnrichmentAnalysisSchema

Return type:

DataFrame[EnrichmentAnalysisSchema]

Example:

result = run_up_down_regulation_enrichment(
    regulation_data,
    annotation,
    identifier='identifier',
    groups=['group1',
    'group2'],
    annotation_col='annotation',
    rejected_col='rejected',
    group_col='group',
    method='fisher',
    correction='fdr_bh',
    alpha=0.05,
    lfc_cutoff=1,
)
run_fisher(group1: list[int], group2: list[int], alternative: str = 'two-sided') → tuple[float, float][source]#

Run fisher’s exact test on two groups using scipy.stats.fisher_exact.

Example:

# annotated   not-annotated
# group1      a               b
# group2      c               d


odds, pvalue = stats.fisher_exact(group1=[a, b],
                                  group2 =[c, d]
                )
run_kolmogorov_smirnov(dist1: list[float], dist2: list[float], alternative: str = 'two-sided') → tuple[float, float][source]#

Compute the Kolmogorov-Smirnov statistic on 2 samples. See scipy.stats.ks_2samp

Parameters:

  • dist1 (list) – sequence of 1-D ndarray (first distribution to compare) drawn from a continuous distribution

  • dist2 (list) – sequence of 1-D ndarray (second distribution to compare) drawn from a continuous distribution

  • alternative (str) – defines the alternative hypothesis (default is ‘two-sided’): * ‘two-sided’ * ‘less’ * ‘greater’

Returns:

statistic float and KS statistic pvalue float Two-tailed p-value.

Example:

result = run_kolmogorov_smirnov(dist1, dist2, alternative='two-sided')

Subpackages#

Submodules#