Differential Expression Analysis of RNA-Seq Data and Co-expression Networks

At present, RNA-seq has become the most common and powerful platform in the study of transcriptomes. A major goal of RNA-seq analysis is the identification of genes and molecular pathways which are differentially expressed in two altered situations. Such difference in expression profiles might be linked with changes in biology giving an indication for further intense investigation. Generally, the traditional statistical methods used in the study of differential expression analysis of gene profiles are restricted to individual genes and do not provide any information regarding interactivities of genes contributing to a certain biological system. This need led the scientists to develop new computational methods to identify such interactions of genes. The most common approach used to study gene-set interactivities is gene network inference. Co-expression gene networks are the correlation-based networks which are commonly used to identify the set of genes significantly involved in the occurrence or presence of a particular biological process. This chapter describes a basic procedure of an RNA-seq analysis along with a brief description about the techniques used in the analysis: an illustration on a real data set is also shown. In addition, a basic pipeline is presented to elucidate how to construct a co-expression network and detect modules from the RNA-seq data.