Challenges Analyzing RNA-Seq Gene Expression Data  

作　　者：Liliana López-Kleine Cristian González-Prieto Liliana López-Kleine;Cristian González-Prieto(Department of Statistics, Universidad Nacional de Colombia—Sede Bogotá, Bogotá, Colombia)

机构地区：[1]Department of Statistics, Universidad Nacional de Colombia—Sede Bogotá, Bogotá, Colombia

出　　处：《Open Journal of Statistics》2016年第4期628-636,共9页统计学期刊（英文）

摘　　要：The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.

关 键 词：RNA-Seq Analysis Count Data PREPROCESSING Differential Expression Gene Co-Expression Network 

分 类 号：R73[医药卫生—肿瘤]