Comparative analysis of differential gene expression tools for RNA sequencing time course data

Abstract

RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.

A simple mass-action model predicts genome-wide protein timecourses from mRNA trajectories during a dynamic response in two strains ofSaccharomyces cerevisiae

“…Although large numbers of genes had detectable levels of mRNA or protein, not all were affected by pheromone induction. We tested for significant induction or repression by checking whether at least one timepoint was significantly different from time 0 (FDR < 0.05) (Spies et al 2019). With some variation between strains, around 25% of analyzed mRNA and 34% of analyzed proteins were significantly induced or repressed, with the majority shared between strains (Table 1; with the color denoting the strain.…”

“…We tested for significant induction or repression by checking whether at least one timepoint was significantly different from time 0 (FDR < 0.05). 21 With some variation between strains around 25% of analyzed mRNA and 35% of analyzed proteins ( Table 1; Figure 1), were significantly induced or repressed, with roughly 55% of these shared between strains ( Table 1). The classification of genes as significantly induced or repressed and/or different between strains at 1 hour post-induction were not, in general, representative of analyses over the whole timecourse ( Figure 2).…”

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

“…We showed that hierarchical clustering was unable to distinguish CM samples from controls or to partition the samples temporally. Other methods currently available to analyze timeseries genomic data (Fischer et al, 2018;Spies et al, 2017;Spies and Ciaudo, 2015;Sun et al, 2016) were not able to: 1) predict the time to develop leukemia, 2) predict the effects of changes in sets of genes over time, or 3) quantify the relative importance of one gene set or pathway over another. Other approaches (i.e., surprisal analysis) that use concepts of thermodynamic (non)equilibrium and statistical mechanics (Facciotti, 2013) are also useful tools for analyzing cellular transitions (e.g., epithelial to mesenchymal transition (Zadran et al, 2014) and early stages of carcinogenesis (Remacle et al, 2010)), but to our knowledge they have not been used to analyze temporal data and do not provide similar geometric-or critical point-based interpretation of the data.…”

“…The analysis and interpretation of such a large volume of data comprising dynamic changes with regard to biological and clinical evolution poses a significant challenge. Although methods are available to analyze time-series genomic data (Bar-Joseph et al, 2012;Sanavia et al, 2015;Spies et al, 2017), to date it has been difficult to meaningfully interpret global gene expression changes over time and use them to predict system dynamics. To achieve the ultimate goal of predicting cancer system dynamics, it is of critical biological and clinical importance to develop a framework guided by a robust theory by which to analyze temporal genomic data.…”

Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis

“…They also discuss the importance of testing a variety of composite hypotheses other than the overall temporal pattern; namely, the nonparallel differentially expressed (NPDE) and parallel differentially expressed (PDE) genes, which are modeled through time by treatment interactions. Other methods for analyzing time-course RNA-seq data include Heinonen et al (2015), Luo et al (2017), Topa and Honkela (2018), and see Spies et al (2019) for a review.…”