alphapepttools.tl.diff_exp_ttest

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alphapepttools.tl.diff_exp_ttest#

alphapepttools.tl.diff_exp_ttest(adata, between_column, comparison, min_valid_values=2, *, layer=None, equal_var=False)#

Calculate ratios of features between two specific groups using t-test.

Calculate ratios and log2 ratios of each feature in the AnnData object between two specific groups defined in the comparison tuple.

Parameters:

  • adata (AnnData) – AnnData object with features and observations.

  • between_column (str) – Name of the column in adata.obs that contains the groups to compare.

  • comparison (tuple) – Tuple of exactly two group names to compare (group1, group2).

  • min_valid_values (int (default: 2)) – Minimum number of samples required per group. By default 2.

  • layer (str | None (default: None)) – Name of the layer in adata.layers to test on. If None (default), adata.X is used.

  • equal_var (bool (default: False)) – Whether to assume equal variance in the t-test. By default False.

Return type:

DataFrame

Returns:

pd.DataFrame DataFrame with ratios, deltas, t-statistics, p-values, and adjusted p-values for the comparison between the two specified groups.

Raises:

ValueError – If layer is not found in adata.layers, or if any check in validate_ttest_inputs fails.

Examples

Run differential expression t-test between treatment groups:

ttest_peptide_results = at.tl.diff_exp_ttest(
    adata=adata_precursor,
    between_column="treatment",
    comparison=("treated", "control"),
    min_valid_values=3,
    equal_var=False,
)

# Access significant peptides
significant = ttest_peptide_results[ttest_peptide_results["fdr"] < 0.05]

Test a normalized layer instead of adata.X:

ttest_peptide_results = at.tl.diff_exp_ttest(
    adata=adata_precursor,
    between_column="treatment",
    comparison=("treated", "control"),
    layer="normalized",
)
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