Abstract
The phenotypic diversity of cells is governed by a complex equilibrium between their genetic identity and their environmental interactions: Understanding the dynamics of gene expression is a fundamental question of biology. However, analysing time-course transcriptomic data raises unique challenging statistical and computational questions, requiring the development of novel methods and software. This workflow provides a step-by-step tutorial of the methodology used to analyse time-course data: (1) quality control and normalization of the dataset; (2) differential expression analysis using functional data analysis; (3) clustering of time-course data; (4) interpreting clusters with GO term and KEGG pathway enrichment analysis. As a case study, we apply this workflow to time-course transcriptomic data from mice exposed to four strains of influenza to showcase every step of the pipeline.
Highlights
Gene expression studies provide simultaneous quantification of the level of mRNA from all genes in a sample
A new variety of time course studies have come from single-cell sequencing experiments (Habib et al, 2016; Shalek et al, 2014; Trapnell et al, 2014) which can sequence single cells at different stages of development; in this case, the time point is the stage of the cell in the process of development -- a value that is not know but estimated from the data as its “pseudo-time.”
We find in practice that the global analysis simplifies analysis and interpretation of longer time courses data, with per-time point analysis reserved for interesting comparisons of individual time-points
Summary
1. Michael I. Love , University of North Carolina at Chapel Hill, Chapel Hill, USA Any reports and responses or comments on the article can be found at the end of the article. Keywords time-course gene expression data, clustering, differential expression, workflow This article is included in the Bioconductor gateway. This article is included in the RPackage gateway.
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