Motivation:
Gene expression profiling using RNA-seq is a powerful technique for
screening RNA species' landscapes and their dynamics in an unbiased
way. While several advanced methods exist for differential expression
analysis of RNA-seq data, proper tools to analyze RNA-seq time-course
have not been proposed.
Results:In this study, we use RNA-seq to measure gene
expression during the early human T helper 17 (Th17) cell
differentiation and T-cell activation (Th0). To quantify Th17-specific
gene expression dynamics, we present a novel statistical methodology,
DyNB, for analyzing time-course RNA-seq data. We use non-parametric
Gaussian processes to model temporal correlation in gene expression and
combine that with negative binomial likelihood for the count data. To
account for experiment-specific biases in gene expression dynamics,
such as differences in cell differentiation efficiencies, we propose a
method to rescale the dynamics between replicated measurements. We
develop an MCMC sampling method to make inference of differential
expression dynamics between conditions. DyNB identifies several known
and novel genes involved in Th17 differentiation. Analysis of
differentiation efficiencies revealed consistent patterns in gene
expression dynamics between different cultures. We use qRT-PCR to
validate differential expression and differentiation efficiencies for
selected genes. Comparison of the results with those obtained via
traditional timepoint-wise analysis shows that time-course analysis
together with time rescaling between cultures identifies differentially
expressed genes which would not otherwise be detected.
The method presented and implemented in this study is available as a MATLAB routine
Tarmo Äijö
Aalto University School of Science,
Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
firstname.lastname@aalto.fi
http://users.ics.aalto.fi/tare/
Updated: May 1, 2015
Updated: August 11, 2014