DupChecker: A bioconductor package for checking high-throughput genomic data redundancy in meta-analysis

Quanhu Sheng, Yu Shyr, Xi Chen

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Background: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis. Results: We developed a Bioconductor package Dupchecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. A real data example was demonstrated to show the usage and output of the package. Conclusions: Researchers may not pay enough attention to checking and removing duplicated samples, and then data contamination could make the results or conclusions from meta-analysis questionable. We suggest applying DupChecker to examine all gene expression data sets before any data analysis step.

Original languageEnglish (US)
Article number323
JournalBMC Bioinformatics
Volume15
Issue number1
DOIs
StatePublished - Sep 30 2014

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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