# OmicsQ: a user-friendly platform for interactive quantitative omics data analysis

## TL;DR

OmicsQ is a web-based platform that simplifies the analysis of complex omics data, offering interactive tools for researchers without programming skills.

## Contribution

OmicsQ introduces an accessible, integrated platform for omics data analysis with robust statistical tools and external application integration.

## Key findings

- OmicsQ handles missing data without imputation, preserving data integrity.
- The platform integrates with external tools for statistical testing and pathway analysis.
- OmicsQ is broadly applicable across domains and accessible via a browser-based interface.

## Abstract

High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values, and individually fluctuating variances. This requires analytical tools for accurate, deep and insightful biological interpretation, capable of dealing with a large variety of data properties and different amounts of completeness. Software capable of handling such data complexity and integrating with external applications for downstream analysis remains rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise.

We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ provides an intuitive, browser-based visualization interface that integrates established statistical processing tools. Those include robust batch correction, automated experimental design annotation, and handling of missing data without imputation, which maintains data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications (e.g. PolySTest, VSClust, ComplexBrowser) for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable across domains.

OmicsQ is implemented in R and Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions: https://github.com/computproteomics/OmicsQ, DOI: 10.5281/zenodo.17778420.