Mitigating the adverse impact of batch effects in sample pattern detection
Author(s) -
Teng Fei,
Tengjiao Zhang,
Weiyang Shi,
Tianwei Yu
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty117
Subject(s) - cluster analysis , computer science , data mining , batch processing , sample size determination , sample (material) , profiling (computer programming) , dimension (graph theory) , artificial intelligence , statistics , mathematics , chromatography , chemistry , pure mathematics , programming language , operating system
It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.
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