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Robinson, M.D. and Oshlack, A. (2010) A Scaling Normalization Method for Differential Expression Analysis of RNA-Seq Data. Genome Biology, 11, R25.
http://dx.doi.org/10.1186/gb-2010-11-3-r25

has been cited by the following article:

  • TITLE: Challenges Analyzing RNA-Seq Gene Expression Data

    AUTHORS: Liliana López-Kleine, Cristian González-Prieto

    KEYWORDS: RNA-Seq Analysis, Count Data, Preprocessing, Differential Expression, Gene Co-Expression Network

    JOURNAL NAME: Open Journal of Statistics, Vol.6 No.4, August 19, 2016

    ABSTRACT: 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.