Data processing inside Omics Playground

Filtering of features (genes) and samples

The data preprocessing includes some filtering criteria, such as filtering of genes based on variance, the expression across the samples, and the number of missing values. Similarly, samples can also be filtered based on the read quality, total abundance, unrelated phenotype, or an outlier criterion.


The raw counts are converted into counts per million (CPM) and log2. Depending on the data set, a quantile normalization can be applied. Known batches in the data can be corrected with limma or ComBat. Other unknown batch effects and unwanted variation can be further removed using surrogate variable analysis in the sva package.

Offline computation

Statistics for the differentially expressed genes analysis and gene set enrichment analysis are precomputed to accelerate the visualisation on the interface.