DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data

Yi Jiang, Gina Giase, Kay Grennan, Annie W Shieh, Yan Xia, Lide Han, Quan Wang, Qiang Wei, Rui Chen, Sihan Liu, Kevin P White, Chao Chen, Bingshan Li and Chunyu Liu

PLOS Computational Biology, 2020, vol. 16, issue 4, 1-19

Abstract: Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007522

DOI: 10.1371/journal.pcbi.1007522

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