Abstract
BackgroundMeta-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.ResultsWe 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.ConclusionsResearchers 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-323) contains supplementary material, which is available to authorized users.
Highlights
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
We developed a bioconductor package DupChecker that can efficiently check sample redundancy based on the raw data files of high-throughput genomic data
For users’ convenience, we developed the functions geoDownload and arrayExpressDownload to download multiple gene expression data sets from Gene Expression Omnibus (GEO) or ArrayExpress databases and deposit the files under the specified directory
Summary
Gene expression meta-analysis has become increasingly popular for high-throughput genomic data analysis. Due to the large amount of publicly available gene expression data contributed by different researchers, it is almost inevitable to include duplicated samples in the data sets collected for meta-analysis. Specimens or RNA samples profiled twice, whether on the sample platform or different platforms, will not be identified using DupChecker In this application note, we illustrated the application using gene expression data, but DupChecker package can be applied to other types of high-throughput genomic data including next-generation sequencing data. Additional file 1: The full result table generated by DupChecker for the colon cancer data. Additional file 2: The full result table generated by DupChecker for the breast cancer data. All authors read and approved the final manuscript
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
