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

作     者：Alexey Stupnikov Galina V Glazko Frank Emmert-Streib 

作者机构：Computational Biology and Machine Learning LaboratoryFaculty of MedicineHealth and Life SciencesSchool of MedicineDentistry and Biomedical SciencesCenter for Cancer Research and Cell BiologyQueen's University Belfast Division of Biomedical InformaticsUniversity of Arkansas for Medical Sciences Computational Medicine and Statistical Learning LaboratoryDepartment of Signal ProcessingTampere University of Technology 

出 版 物：《Chinese Journal of Cancer》 (Chin. J. Cancer)

年 卷 期：2015年第34卷第10期

页      面：427-438页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported In part by the Arkansas Biosciences Institute under Grant(No.UL1TR000039) the IDeANetworks of Biomedical Research Excellence(INBRE) Grant(No.P20RR16460) 

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

摘      要：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.