API

Preprocessing

pp.add_metadata(adata, incoming_metadata, ...)	Add metadata to an AnnData object while checking for matching indices or shape
pp.filter_by_metadata(adata, filter_dict, axis)	Filter based on metadata
pp.filter_data_completeness(adata, max_missing)	Filter features based on missing values
pp.scale_and_center(adata[, scaler, layer, copy])	Scale and center data.
pp.nanlog(data[, base, verbosity, layer, copy])	Logarithmize a data matrix.
pp.detect_special_values(data[, verbosity])	Detect special values such as NaN, zero, negative, and infinite values in the data.
pp.normalize(adata[, layer, strategy, ...])	Normalize measured counts per sample
pp.impute_gaussian(adata[, std_offset, ...])	Impute missing values in each column by random sampling from a gaussian distribution.
pp.impute_median(adata[, group_column, ...])	Impute missing values using median imputation
pp.impute_knn(adata[, group_column, layer, ...])	Impute missing values using median imputation
pp.scanpy_pycombat(adata, batch[, layer, copy])	Wrap scanpy's pp.combat function with error checks and preprocessing suggestions.

Tools

tl.nan_safe_bh_correction(pvals)	Apply Benjamini-Hochberg correction with NaN-safe handling.
tl.nan_safe_ttest_ind(a, b[, min_valid_values])	NaN-safe wrapper around scipy.stats.ttest_ind.
tl.diff_exp_ttest(adata, between_column, ...)	Calculate ratios of features between two specific groups using t-test.
tl.diff_exp_alphaquant(adata, report, ...[, ...])	Calculate differential expression using AlphaQuant.
tl.pca(adata[, layer, dim_space, ...])	Principal component analysis [].
tl.diff_exp_ebayes(adata, between_column, ...)	Run Limma eBayes moderated ttest for differential expression

Metrics

metrics.principal_component_regression(...)	Compute principal component regression (PCR) score.
metrics.pooled_median_absolute_deviation(...)	Compute pooled median absolute deviation (PMAD) within sample groups.

Plotting

pl.Plots([config])	Class for creating figures with matplotlib
pl.add_lines(ax, intercepts[, linetype, ...])	Add a vertical or horizontal line to a matplotlib axes object
pl.label_plot(ax, x_values, y_values, labels)	Add labels to a 2D axes object
pl.BaseColormaps()	Base colormaps for alphapepttools plots
pl.BaseColors()	Base colors for alphapepttools plots
pl.BasePalettes()	Base color palettes for alphapepttools plots
pl.add_legend_to_axes(ax[, levels, legend, ...])	Add a legend to an axis object.
pl.add_legend_to_axes_from_patches(ax, ...)	Make a legend and directly add it to a matplotlib axes object.
pl.create_figure([nrows, ncols, figsize, ...])	Create a figure with a specified number of rows and columns.
pl.label_axes(ax[, xlabel, ylabel, title, ...])	Apply labels to a matplotlib axes object
pl.save_figure(fig, filename, output_dir[, ...])	Save a figure in a publication friendly format

IO

Reader functions

io.read_psm_table(file_paths, search_engine)	Read peptide spectrum match tables to the anndata.AnnData format
io.read_pg_table(path, search_engine, *[, ...])	Read protein group table to the anndata.AnnData format
io.AnnDataFactory(psm_df, intensity_column, ...)	Factory class to convert AlphaBase PSM DataFrames to AnnData format.
io.list_available_reader([kind])	Get a list of all available readers, as provided by alphabase

Data

Example data that can be accessed with the package.

data.available_data()	Get list all available studies
data.get_data(study[, output_dir])	Get data from a specific study