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DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
Yi Jiang1,2,3, Gina Giase4, Kay Grennan5
1Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China.
Plos Computational Biology
|April 14, 2020
Summary
Sample mix-ups in multi-omics studies can lead to errors. We developed DRAMS to detect and correct these sample mix-ups, improving data quality and statistical power for complex disorder research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Multi-omics data integration is crucial for studying complex disorders.
- Sample mix-ups are a common problem in multi-omics studies, compromising data integrity and statistical power.
- Accurate sample identification is essential for reliable multi-omics analyses.
Purpose of the Study:
- To develop a computational tool to detect and correct sample mix-ups in multi-omics datasets.
- To improve the accuracy and reliability of integrative analyses in multi-omics studies.
- To enhance the statistical power and quality of results from multi-omics research.
Main Methods:
- Developed DRAMS (Detect and Re-Align Mixed-up Samples) tool.
- Utilized a logistic regression model and a modified topological sorting algorithm.
- Leveraged relationships within multi-omics data to identify and re-align mixed-up samples.
Main Results:
- DRAMS successfully detected and corrected 201 sample mix-ups (12.5%) in the PsychENCODE BrainGVEX project.
- Corrected 21 mix-ups involving racial identity, aligning data with the 1000 Genomes project.
- Application of DRAMS led to an average 1.62-fold increase in quantitative trait loci (QTL) discovery (FDR<0.01).
Conclusions:
- DRAMS effectively addresses the challenge of sample mix-ups in multi-omics studies.
- The tool enhances statistical power and improves the quality of research findings.
- DRAMS is expected to be increasingly valuable as multi-omics data generation grows.
