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

Author

Listed:
  • 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
  • Chunyu Liu

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

Suggested Citation

  • 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 & Chunyu Liu, 2020. "DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-19, April.
    • Handle: RePEc:plo:pcbi00:1007522
    DOI: 10.1371/journal.pcbi.1007522
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    References listed on IDEAS

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    1. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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    1. Ling Li & Mingming Niu & Alyssa Erickson & Jie Luo & Kincaid Rowbotham & Kai Guo & He Huang & Yuxin Li & Yi Jiang & Junguk Hur & Chunyu Liu & Junmin Peng & Xusheng Wang, 2022. "SMAP is a pipeline for sample matching in proteogenomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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