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DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2020 Apr 13; Vol. 16 (4), pp. e1007522. Date of Electronic Publication: 2020 Apr 13 (Print Publication: 2020). - Publication Year :
- 2020
-
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<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.<br />Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Kevin P. White is employed by Tempus Labs Inc. The other authors have declared that no competing interests exist. This paper has no conflict of interests with Tempus' business.
- Subjects :
- Algorithms
Chromatin chemistry
Computer Simulation
Ethnicity
Female
Genome
Genotype
Humans
Logistic Models
Male
Models, Genetic
Oligonucleotide Array Sequence Analysis
RNA-Seq
Reproducibility of Results
Sex Factors
Software
User-Computer Interface
Whole Genome Sequencing
Computational Biology methods
Frontal Lobe metabolism
Genomics methods
Polymorphism, Single Nucleotide
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 16
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 32282793
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1007522