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POIBM: batch correction of heterogeneous RNA-seq datasets through latent sample matching
- Source :
- Bioinformatics
- Publication Year :
- 2022
- Publisher :
- Oxford University Press (OUP), 2022.
-
Abstract
- Motivation RNA sequencing and other high-throughput technologies are essential in understanding complex diseases, such as cancers, but are susceptible to technical factors manifesting as patterns in the measurements. These batch patterns hinder the discovery of biologically relevant patterns. Unbiased batch effect correction in heterogeneous populations currently requires special experimental designs or phenotypic labels, which are not readily available for patient samples in existing datasets. Results We present POIBM, an RNA-seq batch correction method, which learns virtual reference samples directly from the data. We use a breast cancer cell line dataset to show that POIBM exceeds or matches the performance of previous methods, while being blind to the phenotypes. Further, we analyze The Cancer Genome Atlas RNA-seq data to show that batch effects plague many cancer types; POIBM effectively discovers the true replicates in stomach adenocarcinoma; and integrating the corrected data in endometrial carcinoma improves cancer subtyping. Availability and implementation https://bitbucket.org/anthakki/poibm/ (archived at https://doi.org/10.5281/zenodo.6122436). Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- 11832 Microbiology and virology
Statistics and Probability
Sequence Analysis, RNA
Biochemistry
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Neoplasms
DIFFERENTIAL EXPRESSION ANALYSIS
Exome Sequencing
Humans
1182 Biochemistry, cell and molecular biology
RNA-Seq
Molecular Biology
Software
GENE-EXPRESSION
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....0bde99b8bd8f2bf9807d6e990be66fbe