pl.pheno_level_accumulated_top_n_ORA

Function

pl.pheno_level_accumulated_top_n_ORA(
    target_group,
    method="prod",
    test_type="TER",
    p_adjust=True,
    p_cutoff=0.05,
    n_top_pathway=10,
    n_top_interactions=500,
    piority_term=None,
    eval_para='top_n_ratio',
    dataset_name=None
)

Performs pathway enrichment analysis at the phenotype level using the top-N accumulation strategy. This function evaluates pathway enrichment results across multiple top-N gene sets, ranks pathways based on a specified evaluation parameter, and visualizes the results as a heatmap. It supports prioritization of specific pathways and flexible evaluation metrics.

Parameters

Name	Type	Description
`target_group`	str	Target group for enrichment analysis (e.g., phenotype or cluster name).
`method`	str, optional	Correlation method used for analysis. Default is `"prod"`.
`test_type`	str, optional	Type of statistical test. Default is `"TER"`.
`p_adjust`	bool, optional	Whether to use adjusted p-values for filtering. Default is `True`.
`p_cutoff`	float, optional	P-value cutoff for significance filtering. Default is `0.05`.
`n_top_pathway`	int, optional	Number of top pathways to display in the heatmap. Default is `10`.
`n_top_interactions`	int, optional	Maximum number of top interactions (gene sets) to consider. Default is `500`.
`piority_term`	list or None, optional	List of pathway terms to prioritize or `None` for no prioritization. Default is `None`.
`eval_para`	str, optional	Evaluation parameter for ranking pathways. Options: `'top_n_ratio'`, `'overlap_ratio'`, `'P-value'`, `'Adjusted P-value'`, `'Odds Ratio'`, `'Combined Score'`, `'-logP'`. Default is `'top_n_ratio'`.
`dataset_name`	str or None, optional	Name of the dataset for labeling outputs. Default is `None`.

Return type

None

Returns

Saves a heatmap plot of the top-N pathway enrichment results to the output directory.
Displays the heatmap in the current matplotlib session.
Prints the output file path.

Attributes Set

No new attributes are set on the CRISGI object by this function.

Example

# Assume crisgi_obj is an instance of CRISGI with enrichment results computed

# Perform top-N pathway enrichment analysis for the 'Tumor' group
crisgi_obj.pheno_level_accumulated_top_n_ORA(
    target_group='Tumor',
    method='prod',
    test_type='TER',
    p_adjust=True,
    p_cutoff=0.01,
    n_top_pathway=15,
    n_top_interactions=300,
    piority_term=['Apoptosis', 'Cell Cycle'],
    eval_para='overlap_ratio',
    dataset_name='CancerStudy'
)

# The function will save and display a heatmap of the top 15 pathways
# ranked by overlap ratio for the 'Tumor' group, highlighting prioritized terms.