Analysis options

Data Analysis

The mspypeline package was developed to support the proteomics data analysis workflow while providing both simplicity and flexibility.

The usage of the GUI provides the opportunity to explore and visualize data in a straightforward manner, without demanding any interaction with the code. The GUI offers the majority of plotting options currently available in mspypeline.

Additional plots and more advanced analysis may be performed by specifying optional arguments in the configuration file or by accessing the package as a python module.

Plots

The GUI allows to create the following plots:

Normalization plots:

• Normalization overview: plot_normalization_overview() (exemplary plot in gallery)

• Heatmap overview: plot_heatmap_overview_all_normalizers() (exemplary plot in gallery)

Outlier detection and comparison plots:

• Detection counts: plot_detection_counts() (exemplary plot in gallery)

• Number of detected proteins plot_detected_proteins_per_replicate() (exemplary plot in gallery)

• Venn diagrams: plot_venn_results() (exemplary plot in gallery)

• Group diagrams: plot_venn_groups() (exemplary plot in gallery)

• PCA overview: plot_pca_overview() (exemplary plot in gallery)

• Intensity histogram: plot_intensity_histograms() (exemplary plot in gallery)

• Relative std: plot_relative_std() (exemplary plot in gallery)

• Scatter replicates: plot_scatter_replicates() (exemplary plot in gallery)

• Experiment comparison: plot_experiment_comparison() (exemplary plot in gallery)

• Rank: plot_rank() (exemplary plot in gallery)

Statistical inference plots:

• Pathway analysis: plot_pathway_analysis() (exemplary plot in gallery)

• Go analysis: plot_go_analysis() (exemplary plot in gallery)

• Volcano plot (R): plot_r_volcano() (exemplary plot in gallery)

Additionally via python:

• plot_kde() (exemplary plot in gallery)

• plot_boxplot() (exemplary plot in gallery)

• plot_n_proteins_vs_quantile() (exemplary plot in gallery)

• plot_intensity_heatmap() (exemplary plot in gallery)

Plotters

To perform data analysis and visualisation the Plotters from the MSPlots module are used. The MSPypeline currently contains two Plotters, the mspypeline.BasePlotter and the mspypeline.MaxQuantPlotter.

Base Plotter

The Base Plotter provides all plots listed above. No quality control report is provided.

MaxQuant Plotter

The MaxQuant Plotter is a child class of the Base Plotter and inherits all functionality and plotting options listed above. Additionally, the MaxQuant Plotter provides a quality control report based on supplementary MaxQuant data.

MaxQuant Report

A quality control report for combining information from output tables of MaxQuant. See create_report() for a description of the output and the gallery for an example of such a report.

This quality control report is specifically designed to process available supplementary MaxQuant output tables and generate a multi-page pdf document. Here, the quality of the samples measured by mass spectrometry can be assessed, for instance the shape of a chromatogram or retention time and retention length of peptides. Additionally, information on sample quality such as missed cleavages, number of peptides measured and sequenced, and the proportion of contaminants among protein intensities is provided.