Characterizing the omics landscape based on 10,000+ datasets

Abstract

The characteristics of data produced by omics technologies are pivotal, as they critically influence the feasibility and effectiveness of computational methods applied in downstream analyses, such as data harmonization and differential abundance analyses. Furthermore, variability in these data characteristics across datasets plays a crucial role, leading to diverging outcomes in benchmarking studies, which are the basis for guiding the selection of appropriate analysis methods in all omics fields. At the same time, downstream analysis tools are developed and used in distinct omics communities due to the presumed differing data characteristics arising from the respective omics technology. We investigate on over ten thousand datasets how proteomics, metabolomics, transcriptomics, and microbiome data vary in specific data characteristics. We were able to show patterns of data characteristics specific to the investigated omics types and provide a tool, which allows researchers to characterize and assess how representative a given omics dataset is for an omics discipline. Moreover, we illustrate how data characteristics can impact analysis at the example of normalization in the presence of sample-dependent proportions of missing values. Due to the varying nature of the investigated omics data characteristics, we encourage the inspection of data characteristics in the context of benchmark studies and for downstream analyses to prevent suboptimal method selection and unintended bias.