CAGEE: Computational Analysis of Gene Expression Evolution

Abstract

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (CAGEE) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data, and apply it to a dataset of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

“…Specifically, we leverage single-cell full-length RNA sequencing data to investigate the role of selection on gene expression and function in subclonal evolution of cancer. We model clonal gene expression evolution using stochastic Ornstein-Uhlenbeck (OU) processes which have been previously applied in the context of species evolution 17,18,19,20,21,22,13 and cellular differentiation 11 , but have not been utilized in studies on subclonal evolution. Our use of OU processes allows us to model changes in subclone-specific expression along trajectories defined by the evolutionary history of the subclones.…”

A tale of too many trees: a conundrum for phylogenetic regression

“…However, this assumption may (Dimayacyac et al 2023) or may not (Hahn and Nakhleh 2016a) be the best strategy. On the other hand, modeling evolution as a function of a particular gene tree may prove beneficial for traits predicted to exhibit a more direct one-to-one correspondence with a single tree, such as expression of a gene largely regulated by cis elements near its encoded locus (Chen et al 2019;Bertram et al 2022;Bastide et al 2023;Dimayacyac et al 2023).…”

“…Perhaps such scenarios arguably represent a best case in which we might at least hope to match tree with trait. Fitting PCMs to gene expression data has also garnered recent considerable interest for modeling functional genomic evolution across cells, tissues, and species (Chen et al 2019;Bertram et al 2022;Adams et al 2023;Bastide et al 2023;Dimayacyac et al 2023). Somewhat surprisingly, a recent study found that modeling gene expression as a function of the overall species tree improved the fit of Ornstein-Uhlenbeck models (Dimayacyac et al 2023), even when compared to local gene trees.…”

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

“…In addition to controlling for unobserved (and phylogenetically structured) confounding variables, phylogenetic comparative methods (PCMs; for reviews of these methods see Pennell and Harmon 2013 ; Harmon 2019 ) are increasingly being used to characterize the evolutionary dynamics of gene expression over time, for example, by looking for the signature of selection in the distribution of gene expression values at the tips ( Oakley et al 2005 ; Bedford and Hartl 2009 ; Brawand et al 2011 ; Dunn et al 2013 ; Rohlfs et al 2014 ; Rohlfs and Nielsen 2015 ; Price et al 2022 ). And accordingly, there have been a number of recent methodological developments, including computational platforms for simulating ( Bastide et al 2023 ) and analyzing ( Bertram et al 2023 ) phylogenetic comparative gene expression datasets.…”