A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis

Author:

Lee Eunjee123ORCID,Yoo Seungyeul12,Wang Wenhui12,Tu Zhidong12,Zhu Jun1234ORCID

Affiliation:

1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA

2. Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA

3. Sema4, a Mount Sinai venture, 333 Ludlow street, Stamford, CT 06902, USA

4. The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA

Abstract

Abstract Background Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases. Results Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis. Conclusions Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

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