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Big data mining yields novel insights on cancer

Nature Genetics volume 47, pages 103–104 (2015)Cite this article

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Recent years have seen the rapid growth of large-scale biological data, but the effective mining and modeling of 'big data' for new biological discoveries remains a significant challenge. A new study reanalyzes expression profiles from the Gene Expression Omnibus to make novel discoveries about genes involved in DNA damage repair and genome instability in cancer.

Since the invention of gene expression microarray technology almost 20 years ago, numerous mRNA profiling data sets have been generated for diverse biological processes in many organisms. Currently, there are over 30,000 series and 1 million samples of array-based gene expression data deposited in the NCBI Gene Expression Omnibus (GEO) database. In this issue, Rudolf Fehrmann and colleagues comprehensively reanalyzed the expression profiles of 77,840 Affymetrix gene expression data sets from GEO, using principal-components analysis (PCA) to identify 'transcriptional components' , which each capture a part of the variance seen in gene expression across samples1. Using this test set of samples, the authors developed a method for extracting biological information about the regulatory program of the samples. They then used this method to analyze expression data from 16,172 tumor samples for cancer biology discovery.

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Figure 1: PCA of gene expression.

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Authors and Affiliations

  1. Peng Jiang and X. Shirley Liu are in the Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts, USA.,

    Peng Jiang & X Shirley Liu

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  1. Peng Jiang
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  2. X Shirley Liu
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Correspondence to X Shirley Liu.

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Jiang, P., Liu, X. Big data mining yields novel insights on cancer. Nat Genet 47, 103–104 (2015). https://doi.org/10.1038/ng.3205

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