New tools for time series analysis of 'omics' data

Abstract

It is peak hour in downtown Transcriptome, where millions of messenger RNA are en route from their local DNA carrying important instructions for protein synthesis and other civic services. A typical transcriptomics (RNA-seq) data set is a snapshot of this busy scene, comprising a sample of extracts of these instruction sequences. A key goal in molecular biology is to determine which sequences (i.e., sets of transcripts or genes) change their expression in response to different treatment conditions to discover molecular mechanisms for biological traits. In this talk, I will give a brief background on the sampling, technical, and statistical processes involved in generating such data sets before focusing on new and existing methods for their analysis when generated from time series experiments. The initial modelling goals are to determine which transcripts are differentially expressed, at what time after treatment, and with what type of trend. To make progress, we combine changepoint analysis with shape-constrained smooth additive models while taking care to propagate estimation uncertainty and to control the false discovery rate. The latter is a perennial challenge in ‘omics’ data analysis where a vast number of distinct targets and thus statistical comparisons must be made from just a small number of samples.