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alphapepttools

  • API
    • alphapepttools.pp.add_metadata
    • alphapepttools.pp.filter_by_metadata
    • alphapepttools.pp.filter_data_completeness
    • alphapepttools.pp.scale_and_center
    • alphapepttools.pp.nanlog
    • alphapepttools.pp.detect_special_values
    • alphapepttools.pp.normalize
    • alphapepttools.pp.impute_gaussian
    • alphapepttools.pp.impute_median
    • alphapepttools.pp.impute_knn
    • alphapepttools.pp.impute_bpca
    • alphapepttools.pp.scanpy_pycombat
    • alphapepttools.tl.get_id2gene_map
    • alphapepttools.tl.map_genes_to_protein_groups
    • alphapepttools.tl.nan_safe_bh_correction
    • alphapepttools.tl.nan_safe_ttest_ind
    • alphapepttools.tl.diff_exp_ttest
    • alphapepttools.tl.diff_exp_alphaquant
    • alphapepttools.tl.pca
    • alphapepttools.tl.bpca
    • alphapepttools.tl.diff_exp_ebayes
    • alphapepttools.tl.extract_pca_anndata
    • alphapepttools.tl.prepare_pca_1d_loadings_data_to_plot
    • alphapepttools.tl.prepare_pca_2d_loadings_data_to_plot
    • alphapepttools.tl.prepare_scree_data_to_plot
    • alphapepttools.tl.prepare_pca_1d_loadings_data_to_plot
    • alphapepttools.metrics.coefficient_of_variation
    • alphapepttools.metrics.principal_component_regression
    • alphapepttools.metrics.pooled_coefficient_of_variation
    • alphapepttools.metrics.pooled_median_absolute_deviation
    • alphapepttools.metrics.calculate_qc_metrics
    • alphapepttools.metrics.fraction_complete
    • alphapepttools.metrics.number_detected
    • alphapepttools.metrics.total_intensity
    • alphapepttools.pl.Plots
    • alphapepttools.pl.add_lines
    • alphapepttools.pl.label_plot
    • alphapepttools.pl.BaseColormaps
    • alphapepttools.pl.BaseColors
    • alphapepttools.pl.BasePalettes
    • alphapepttools.pl.add_legend_to_axes
    • alphapepttools.pl.add_legend_to_axes_from_patches
    • alphapepttools.pl.create_figure
    • alphapepttools.pl.label_axes
    • alphapepttools.pl.save_figure
    • alphapepttools.pl.get_color_mapping
    • alphapepttools.io.read_psm_table
    • alphapepttools.io.read_pg_table
    • alphapepttools.io.AnnDataFactory
    • alphapepttools.io.list_available_reader
    • alphapepttools.data.available_data
    • alphapepttools.data.get_data
  • Tutorials
    • Bader et al, 2020: Analysis of body fluid proteomics
    • Li et al, 2025: Analysis of peptidomics data (PELSA)
    • Stadler et al, 2025: Analysis of bulk proteomics data
    • Weiss et al, 2026: Single-cell spatial proteomics on human liver zonation
    • Introduction to proteomics data analysis with anndata
    • Example alphapepttools workflow with proteomics data
    • Scientific visualizations with alphapepttools
    • Principal component analysis
    • Filtering by metadata
  • Changelog
  • Contributing guide
  • References
  • .md

Tutorials

Contents

  • End-to-end workflows
  • Functional steps

Tutorials#

Learn about how to use alphapepttools with the following tutorials.

End-to-end workflows#

These notebooks analyse a dataset end-to-end and provide a full overview

  • Bader et al, 2020: Analysis of body fluid proteomics
  • Li et al, 2025: Analysis of peptidomics data (PELSA)
  • Stadler et al, 2025: Analysis of bulk proteomics data
  • Weiss et al, 2026: Single-cell spatial proteomics on human liver zonation

Functional steps#

These notebooks center around a specific analysis step or functionality of the package

  • Introduction to proteomics data analysis with anndata
  • Example alphapepttools workflow with proteomics data
  • Scientific visualizations with alphapepttools
  • Principal component analysis
  • Filtering by metadata

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alphapepttools.data.get_data

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Bader et al, 2020: Analysis of body fluid proteomics

Contents
  • End-to-end workflows
  • Functional steps

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