1Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany2Cluster of Excellence MMCI, Saarland University, Saarland Informatics Campus, Saarbrücken, Germany3Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany4Genome Institute of Singapore, Computational Genomics and Transcriptomics, Singapore5Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany6Berlin Institute of Health (BIH), Berlin, Germany7Institute for Cardiovascular Regeneration, Goethe University, Frankfurt am Main, Germany8German Center for Cardiovascular Research, Partner Site Rhein-Main, Frankfurt am Main, Germany
Checking for direct PDF access through Ovid
MotivationInternational consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.ResultsWe present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.Availability and implementationOur method is available online at https://github.com/SchulzLab/OntologyEval.Supplementary informationSupplementary data are available at Bioinformatics online.