OmicsQ: a user-friendly and interactive platform for quantitative proteomics data analytics

Quantitative analysis and biological interpretation of proteomics data require appropriate statistical methods and careful assessment of their outputs. There is a lack of user-friendly, interactive toolkits for supervised analysis of quantitative peptide and protein expression profiles. Additionally, common tools often rely on value imputation, potentially leading to overestimation of statistical power and inclusion of erroneous values.

We developed a user-friendly software toolkit for analyzing quantitative peptide or protein profiles from textual or Excel files. The toolkit includes modern methods for statistical testing, appropriate handling of missing data, quantitative QC, clustering of quantitative profiles, and investigating the quantitative behavior of protein complexes. Features include consistency checks of uploaded data, filtering unsuitable samples, batch correction, statistical power harmonization, and automatic sample group assignment. The underlying tools have been updated to work with missing data without need for imputation. Protein complex analysis with ComplexBrowser and CoExpresso now integrate recent data from Complex Portal and CORUM. Statistical testing with PolySTest and clustering with VSClust have been turned into Bioconductor packages. The platform is fully open source and can also be deployed locally. OmicsQ is available as a web service via https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ.

The OmicsQ toolkit enables deep proteomics analysis with minimal user effort, facilitating extensive biological interpretation and visualization. It features statistical testing without imputation, variance-sensitive clustering for improved recognition of co-expressed feature groups, and comprehensive analysis of protein complex behavior through the co-regulative behavior of their subunits. Running as a web browser application, it requires no local installation. Broad parameterization options and extensive visualizations provide full control over the analysis, easing the exposure of relevant content for a given biological context. We demonstrate the power of the toolkit on various proteomics datasets, showcasing its ability for deep characterization in different biological systems.

In summary, OmicsQ offers a user-friendly, powerful solution for in-depth investigation of biological processes in multi-dimensional proteomics datasets.