CONSTANd: An Efficient Normalization Method for Relative Quantification in Small- and Large-Scale Omics Experiments in R BioConductor and Python

For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.

Automated summary

Data normalization is crucial for differential expression studies in omics, and the CONSTANd method offers a fast and effective solution for researchers in different omics contexts. Adoption of this method could facilitate data integration in multiomics experiments.