Omics Playground is an online self-service analytics platform that empowers life scientists to perform complex omics data analysis and visualization by themselves, without the need to learn how to code.

The platform focuses on the tertiary analysis (i.e. interpretation) of transcriptomics (e.g. micro-array, RNA-seq, scRNA-seq) and proteomics data, providing you with highly interactive visualization tools to understand your data.

More than 100 HQ plots

More than 18 interactive modules


Best-in-class algorithms

Explore some of the detailed functionalities the platform offers:

Expression and enrichment analysis

For differential expression, Omics Playground uses up to eight different statistical testing methods. For gene set enrichment analysis, we use more than 50’000 gene sets with up to seven different statistical methods. We combine multiple statistical methods using meta-analysis so you can really trust your numbers. You can enter custom gene sets to perform enrichment analysis of your own signature.

Drug enrichment analysis

We also perform drug enrichment analysis against more than 5’000 drug expression profiles, and find drugs (both on their own and in combination) with similar or opposing signatures compared to yours.

Variable importance and biomarker selection

To understand which genes or gene sets are more influential on the outcome of your study, the Playground platform calculates variables importance using state-of-the-art machine learning algorithms. The outcome can be multiple categories (classes) or patient survival. Using several algorithms we select the best biomarkers. We then train classifiers to build a prediction model for your data and phenotype of interest.

Special immuno-oncology analytics

We have implemented a special analysis module for immuno-oncology. You can infer copy number variation from single-cell RNA-seq expression, analyze differential immuno-genes usage and perform computational immune cell type profiling using state-of-the-art deconvolution algorithms.

Advanced batch effects analysis

Omics data are notoriously sensitive to so-called “batch effects” that can make or break your analysis. Within the Playground you can easily identify batch effects with our unique PCA cluster analysis. Quantify batch effects and visually evaluate a variety of batch correction techniques in real time.

See also Viromics Playground, Rep-seq Playground (BETA) and MultiOmics Playground (BETA).