alphapepttools.tl.diff_exp_alphaquant

alphapepttools.tl.diff_exp_alphaquant(adata, report, between_column, comparison, min_valid_values=2, valid_values_filter_mode='either', plots='hide')

Calculate differential expression using AlphaQuant.

Parameters:

• adata (AnnData) – AnnData object containing the expression data and sample metadata.

• report (DataFrame) – DataFrame with quantification report data for AlphaQuant analysis.

• between_column (str) – Column name in adata.obs containing group labels for comparison.

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

• min_valid_values (int (default: 2)) – Minimum number of valid values required per group for statistical testing.

• valid_values_filter_mode (str (default: 'either')) – How to apply the min_valid_values filter - ‘either’ or ‘both’.

• plots (str (default: 'hide')) – Whether to ‘show’ or ‘hide’ AlphaQuant’s generated plots.

Return type:

tuple[str, dict[str, DataFrame]]

Returns:

tuple[str, dict[str, pd.DataFrame]] Tuple containing: - comparison_key: String identifier for the comparison (e.g., “Group1_VS_Group2”) - results: Dictionary with keys ‘protein’, ‘peptide’, ‘proteoform’ containing

standardized differential expression results for each level.

Raises:

• ImportError – If alphaquant is not installed.

• ValueError – If plots is not ‘hide’ or ‘show’, if between_column is not in adata.obs, or if comparison is not a tuple of exactly two elements.

Examples

Run differential expression analysis between treatment groups:

comparison_key, alphaquant_results = at.tl.diff_exp_alphaquant(
    adata=adata_precursor,
    report=full_report,
    between_column="treatment",
    comparison=("control", "treated"),
    valid_values_filter_mode="either",
    min_valid_values=3,
    plots="hide",
)

# Access results for different levels
protein_results = alphaquant_results["protein"]
peptide_results = alphaquant_results["peptide"]
proteoform_results = alphaquant_results["proteoform"]