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

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• 作者：Quanhu Sheng (24) (25)
  Yu Shyr (25) (26)
  Xi Chen (25) (26)
  
  24. Department of Cancer Biology ; Vanderbilt University School of Medicine ; Nashville ; TN ; 37232 ; USA
  25. Center for Quantitative Sciences ; Vanderbilt University School of Medicine ; Nashville ; TN ; 37232 ; USA
  26. Department of Biostatistics ; Vanderbilt University School of Medicine ; Nashville ; TN ; 37232 ; USA
  
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：219 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

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.