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Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients

Chinese Journal of Cancer 2015年第10期34卷 427-438页

作者： Alexey Stupnikov Galina V Glazko frank emmert-streib Computational Biology and Machine Learning Laboratory Faculty of MedicineHealth and Life SciencesSchool of MedicineDentistry and Biomedical SciencesCenter for Cancer Research and Cell BiologyQueen's University Belfast Division of Biomedical Informatics University of Arkansas for Medical Sciences Computational Medicine and Statistical Learning Laboratory Department of Signal ProcessingTampere University of Technology

Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an ***,the analysis of such data is very *** this study,we aimed to establish robust analysis procedures that can be used in clinical ***:We studied RNA-seq data from triple-negative breast cancer ***,we investigated the subsampling of RNA-seq ***:The main results of our investigations are as follows:(1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices;(2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads;and(3) for an abrogated feature selection,higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean ***:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.

关键词： RNA-seq data, Computational genomics, Statistical robustness, High-dimensional biology,Triple-negative breast cancer

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