Differential Expression Analysis
Differential expression analysis means taking RNA-Seq expression data and performing statistical analysis to discover quantitative changes in gene expression levels between experimental groups. In other words, differential expression analysis consists of the identification of genes (or other types of genomic features, such as transcripts or exons) that are expressed in significantly different quantities in distinct groups of samples. There are several statistical methodologies that allow addressing different experimental designs: biological conditions, diseased vs healthy, different tissues, different development stages, different gender, time series…
Three strategies are available:
Pairwise Differential Expression Analysis: Pairwise differential expression analysis allows the identification of differentially expressed genes considering different experimental conditions studied in RNA-Seq experiments. It is based on the software package “edgeR” (empirical analysis of DGE in R), which belongs to the Bioconductor project.
Pairwise Differential Expression Analysis (Without Replicates): Pairwise differential expression analysis (without replicates) is suited when there are not replicates for any of the experimental conditions. It is based on the Bioconductor software package “NOISeq”, which can compare samples from two experimental conditions by simulating replicates.
Time Course Expression Analysis: Based on the software package “maSigPro”, which belongs to the Bioconductor project, time-course expression analysis detects genes for which there are significant expression profile differences in time course RNA-Seq experiments.
