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(adata[, 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[, group_column, ...])	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.impute_bpca(adata, *[, n_components, ...])	Impute missing values using Bayesian Principal Component Analysis (BPCA)
pp.scanpy_pycombat(adata, batch[, layer, copy])	Correct batch effects using the ComBat method [].
pp.drop_singleton_batches(adata, batch)	Drop samples from batches that contain only a single sample.

Tools

tl.get_id2gene_map(fasta_input[, source_type])	Reannotate protein groups with gene names from a FASTA input.
tl.map_genes_to_protein_groups(id2gene_map, ...)	Map gene names to protein groups using the provided id2gene_map mapping
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.diff_exp_ebayes(adata, between_column, ...)	Run Limma eBayes moderated ttest for differential expression.
tl.pca(adata[, layer, dim_space, ...])	Principal component analysis [].
tl.bpca(adata[, layer, dim_space, ...])	Bayesian Principal Component Analysis
tl.extract_pca_anndata(adata[, dim_space, ...])	Extract PCA/BPCA data required for plotting from an AnnData object.
tl.prepare_pca_1d_loadings_data_to_plot(...)	Prepare the gene loadings (1d) of a PC for plotting.
tl.prepare_pca_2d_loadings_data_to_plot(...)	Prepare a DataFrame with PCA feature loadings for the 2D plotting.
tl.prepare_scree_data_to_plot(adata, n_pcs, ...)	Prepare scree plot data from AnnData object.

Metrics

metrics.coefficient_of_variation(adata, *[, ...])	Coefficient of variation
metrics.principal_component_regression(...)	Compute principal component regression (PCR) score.
metrics.pooled_coefficient_of_variation(...)	Compute pooled coefficient of variation within sample groups.
metrics.pooled_median_absolute_deviation(...)	Compute pooled median absolute deviation (PMAD) within sample groups.
metrics.calculate_qc_metrics(adata, *[, layer])	Calculate all QC metrics and add them to adata.obs.
metrics.fraction_complete(adata, *[, layer, ...])	Calculate the fraction of detected values per observation or per feature.
metrics.number_detected(adata, *[, layer, ...])	Count the number of detected features per observation or detected observations per feature.
metrics.total_intensity(adata, *[, layer, ...])	Calculate sum of intensity per observation or per feature.

Plotting

pl.Plots(*args, **kwargs)	Removed.
pl.add_lines(ax, intercepts[, linetype, ...])	Add vertical or horizontal reference lines to a plot
pl.label_plot(ax, data, x_column, y_column, ...)	Add labels to a 2D axes object
pl.BaseColormaps()	Continuous colormaps for alphapepttools plots
pl.BaseColors()	Default color palette for alphapepttools plots
pl.BasePalettes()	Discrete color palettes for alphapepttools plots
pl.MappedColormaps(cmap[, percentile])	Percentile-based colormap normalization for outlier-robust visualization
pl.show_rgba_color_list(colors)	Display a horizontal bar of RGBA colors for visual inspection
pl.PlotConfig([data, _extra])	Base configuration for all plot types
pl.make_scatter_config(data, x_column, ...)	Create a config for scatter plots
pl.add_legend_to_axes(ax[, levels, legend, ...])	Flexibly add a legend to axes with automatic color assignment
pl.add_legend_to_axes_from_patches(ax, ...)	Add a legend with patches to an axes, using config defaults for font sizes
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 formatted labels and optional enumeration to a matplotlib axes object
pl.save_figure(fig, filename, output_dir[, ...])	Save a figure in a publication-friendly format
pl.get_color_mapping(values, palette)	Map categorical values to colors
pl.layered_plot(ax, base_config[, layers, ...])	Plot multiple layers with defined hierarchy and without datapoint reuse.
pl.histogram(data, value_column[, ...])	Plot a histogram from a DataFrame or AnnData object
pl.scatter(data, x_column, y_column[, ...])	Plot a scatterplot from a DataFrame or AnnData object
pl.barplot(ax, data[, grouping_column, ...])	Plot a bar chart from a DataFrame or AnnData object
pl.boxplot(ax, data[, grouping_column, ...])	Plot a box plot from a DataFrame or AnnData object
pl.violinplot(ax, data[, grouping_column, ...])	Plot a violin plot from a DataFrame or AnnData object
pl.rank_median_plot(data, ax[, layer, ...])	Rank plot showing median intensities across samples.
pl.plot_pca(data[, x_column, y_column, ...])	PCA scatter plot showing principal component projections.
pl.scree_plot(adata, ax[, n_pcs, dim_space, ...])	Scree plot showing explained variance for each principal component.
pl.plot_pca_loadings(data, ax[, dim_space, ...])	1D loadings plot showing top features contributing to a principal component.
pl.plot_pca_loadings_2d(data, ax[, ...])	2D loadings plot showing top features contributing to two principal components.
pl.volcano(data[, x_column, y_column, ax, ...])	Create a volcano plot for differential expression visualization

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()	List all available proteomics studies
data.get_data(study[, output_dir])	Download data from a specific study