AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics

Abstract

Summary The widespread application of mass spectrometry (MS)-based proteomics in biomedical research increasingly requires robust, transparent and streamlined solutions to extract statistically reliable insights. We have designed and implemented AlphaPeptStats, an inclusive python package with currently with broad functionalities for normalization, imputation, visualization, and statistical analysis of label-free proteomics data. It modularly builds on the established stack of Python scientific libraries, and is accompanied by a rigorous testing framework with 98% test coverage. It imports the output of a range of popular search engines. Data can be filtered and normalized according to user specifications. At its heart, AlphaPeptStats provides a wide range of robust statistical algorithms such as t-tests, ANOVA, PCA, hierarchical clustering and multiple covariate analysis—all in an automatable manner. Data visualization capabilities include heat maps, volcano plots, scatter plots in publication-ready format. AlphaPeptStats advances proteomic research through its robust tools that enable researchers to manually or automatically explore complex datasets to identify interesting patterns and outliers. Availability AlphaPeptStats is implemented in Python and part of the AlphaPept framework. It is released under a permissive Apache license. The source code and one-click installers are freely available and on GitHub at https://github.com/MannLabs/alphapeptstats. Supplementary information Supplementary data are available at Bioinformatics online.

AlphaPept: a modern and open framework for MS-based proteomics

“…Currently, AlphaPept provides functionality for DDA proteomics, but we are in the process of enabling analysis of DIA data, ultra-fast access to and visualization of ion mobility data (AlphaTims 26 ,https://github.com/MannLabs/alphatims), deep learning for predicted peptide properties (AlphaPeptDeep 50 , https:// github.com/MannLabs/alphapeptdeep), Visualization of search engine results (AlphaViz, https://github.com/MannLabs/alphaviz), visual annotation of results (AlphaMap 66,67 , https://github.com/ MannLabs/alphamap), statistical downstream analysis (AlphaPept Stats 68 ) and improved quantification, all made possible by its modular design. While AlphaPept may not be best-in-class across all benchmarks, it is on par with widely used proteomics tools but has the benefit of being completely open and permissively licensed.…”

Deciphering functional tumor-immune crosstalk through highly multiplexed imaging and deep visual proteomics

“…Remaining missing values were imputed using the K-Nearest Neighbors (KNN) imputation method 43 . All samples' intensity values underwent a logarithmic transformation (log2) and were used for principal component analysis (PCA) and identification of differentially expressed proteins.…”

“…The proteome datasets underwent filtering to include samples with at least 70% valid values, excluding proteins with more than 30% missing values for subsequent statistical analyses. Remaining missing values were imputed using the K-Nearest Neighbors (KNN) imputation method 43 . All samples’ intensity values underwent a logarithmic transformation (log2) and were used for principal component analysis (PCA) and identification of differentially expressed proteins.…”

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

“…For example, NormalyzerDE, an R package, includes several popular methods for normalization and differential expression analysis of LC-MS data. 717 AlphaPeptStats, 718 a Python package, allows for comprehensive mass spectrometry data analysis, including normalization, imputation, batch correction, visualization, statistical analysis and graphical representations including heatmaps, volcano plots, and scatter plots. AlphaPeptStats allows for analysis of label-free proteomics data from several platforms (MaxQuant, AlphaPept, DIA-NN, Spectronaut, FragPipe) in Python but also has web version that does not require installation.…”