Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis p...Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis procedures that can be used in clinical practice.Methods:We studied RNA-seq data from triple-negative breast cancer patients.Specifically,we investigated the subsampling of RNA-seq data.Results: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 values.Conclusions:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.展开更多
基金supported In part by the Arkansas Biosciences Institute under Grant(No.UL1TR000039)the IDeANetworks of Biomedical Research Excellence(INBRE) Grant(No.P20RR16460)
文摘Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis procedures that can be used in clinical practice.Methods:We studied RNA-seq data from triple-negative breast cancer patients.Specifically,we investigated the subsampling of RNA-seq data.Results: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 values.Conclusions:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.
