DRAMS: A Tool to Detect and Re-Align Mixed-up Samples for Integrative Studies of Multi-omics Data

Jiang, Y.; Giase, G.; Grennan, K.; Shieh, A. W.; Xia, Y.; Han, L.; Wang, Q.; Wei, Q.; Chen, R.; Liu, S.; White, K. P.; Chen, C.; Li, B.; Liu, C.

doi:10.1101/831537

bioRxiv · 10.1101/831537

DRAMS: A Tool to Detect and Re-Align Mixed-up Samples for Integrative Studies of Multi-omics Data

Jiang, Y.·Giase, G.·Grennan, K.·Shieh, A. W.·Xia, Y.·Han, L.·Wang, Q.·Wei, Q.·Chen, R.·Liu, S.·White, K. P.·Chen, C.·Li, B.·Liu, C.

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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 samples to the correct racial cluster in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.\n\nAuthor summarySample mix-up happens inevitably during sample collection, processing, and data management. It leads to reduced statistical power and sometimes false findings. It is of great importance to correct mixed-up samples before conducting any downstream analyses. We developed DRAMS to detect and re-align mixed-up samples in multi-omics studies. The basic idea of DRAMS is to align the data and labels for each sample leveraging the genetic information of multi-omics data. DRAMS corrects sample IDs following a two-step strategy. At first, it estimates pairwise genetic relatedness among all the data generated from all the individuals. Because the different data generated from the same individual should share the same genetics, we can cluster all the highly related data and consider that the data from one cluster have only one potential ID. Then, we used a \"majority vote\" strategy to infer the potential ID for individuals in each cluster. Other information, such as match of genetics-based and reported sexes, omics priorority, etc., were also used to direct identifying the potential IDs. It has been proved that DRAMS performs very well in both simulation and PsychENCODE BrainGVEX multi-omics data.

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Jiang, Y., Giase, G., Grennan, K., Shieh, A. W., Xia, Y., Han, L., Wang, Q., Wei, Q., Chen, R., Liu, S., White, K. P., Chen, C., Li, B., Liu, C.. 2019-11-06. DRAMS: A Tool to Detect and Re-Align Mixed-up Samples for Integrative Studies of Multi-omics Data. https://doi.org/10.1101/831537

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