Share This Article:

Challenges Analyzing RNA-Seq Gene Expression Data

Full-Text HTML XML Download Download as PDF (Size:1666KB) PP. 628-636
DOI: 10.4236/ojs.2016.64053    1,551 Downloads   2,384 Views Citations


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.

Cite this paper

López-Kleine, L. and González-Prieto, C. (2016) Challenges Analyzing RNA-Seq Gene Expression Data. Open Journal of Statistics, 6, 628-636. doi: 10.4236/ojs.2016.64053.

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.