scbulkde.DEResult

scbulkde.DEResult#

class scbulkde.DEResult(results, query, reference, design, engine, used_pseudoreplicates=False, used_single_cell=False, n_repetitions=1, repetition_results=<factory>, repetition_stats=<factory>)#

Container for differential expression results.

results#

DE results table with columns: log2FoldChange, pvalue, padj, stat, stat_sign.

Type:: pd.DataFrame

query#

Query condition name.

Type:: str

reference#

Reference condition name(s).

Type:: str or list[str]

design#

Design formula used for testing.

Type:: str

engine#

Name of the DE engine used (e.g. 'anova', 'pydeseq2').

Type:: str

used_pseudoreplicates#

Whether pseudoreplicates were generated to meet the minimum sample requirement.

Type:: bool

used_single_cell#

Whether single-cell level testing was performed as a fallback.

Type:: bool

n_repetitions#

Number of pseudoreplicate iterations run. 1 for direct testing, >1 when pseudoreplicates are used.

Type:: int

repetition_results#

Per-repetition DE results. Only populated when pseudoreplicates are used.

Type:: dict[str, pd.DataFrame]

repetition_stats#

Per-repetition sample statistics. Only populated when pseudoreplicates are used.

Type:: dict[str, pd.DataFrame]

Attributes table#

DEResult.fallback_used: Return which fallback was used, if any.

DEResult.n_genes: Number of genes tested.

DEResult.n_repetitions: int = 1

DEResult.n_significant: Number of significant genes (padj < 0.05).

DEResult.used_pseudoreplicates: bool = False

DEResult.used_single_cell: bool = False

DEResult.results: DataFrame

DEResult.query: str

DEResult.reference: str | list[str]

DEResult.design: str

DEResult.engine: str

DEResult.repetition_results: dict[str, DataFrame]

DEResult.repetition_stats: dict[str, DataFrame]

Methods table#

`get_repetition_results`(repetition)	Get DE results for a specific repetition.
`get_repetition_stats`(repetition)	Get sample statistics for a specific repetition.
`summary`()	Return the main results table.

Attributes#

DEResult.fallback_used#: Return which fallback was used, if any.

DEResult.n_genes#: Number of genes tested.

DEResult.n_repetitions: int = 1#

DEResult.n_significant#: Number of significant genes (padj < 0.05).

DEResult.used_pseudoreplicates: bool = False#

DEResult.used_single_cell: bool = False#

DEResult.results: DataFrame#

DEResult.query: str#

DEResult.reference: str | list[str]#

DEResult.design: str#

DEResult.engine: str#

DEResult.repetition_results: dict[str, DataFrame]#

DEResult.repetition_stats: dict[str, DataFrame]#

Methods#

DEResult.get_repetition_results(repetition)#

Get DE results for a specific repetition.

Parameters:

repetition (int or str) – The repetition index or key to retrieve.

Return type:

DataFrame

Returns:

pd.DataFrame DE results for the requested repetition.

Raises:

ValueError – If no pseudoreplicates were used.
KeyError – If the specified repetition is not found.

DEResult.get_repetition_stats(repetition)#

Get sample statistics for a specific repetition.