Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

High throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for profiling detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time-points. We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene’s expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-seq datasets. Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available as an R package with a full vignette at https://github.com/rhondabacher/Trendy. Background High throughput expression profiling technologies have become essential tools for advancing insights into biological systems and processes. By profiling over ordered conditions such as time or space, the power of microarrays and sequencing can be further leveraged to study the dynamics of biological processes. Of great interest in time-course or spatial-course experiments is to identify genes with dynamic expression patterns, which can provide insight on regulatory genes (1) and highlight key transitional periods (2). Many methods for time-course experiments are aimed at identifying differentially expressed genes across multi-series time-courses and are detailed in a review by Spies and Ciaudo, 2015 (3). Here we focus on analyzing single-series time-course experiments, where one can identify dynamic genes via each genes’s expression path or pattern across time. Single-series time course methods have largely focused on the clustering of gene expression (4; 5), which can be used to construct regulatory networks, and they do not typically emphasize the information provided by each gene’s individual expression path. EBSeqHMM (6) was developed in part to address this deficiency and employs a hidden Markov model to classify genes into distinct expression paths. Despite its utility, differences between time-points may not be sufficiently detectable for extensive or densely sampled time-course experiments with subtle expression changes. FunPat (7) is another method which can analyze a single-series time course and does so based on comparing changes in expression to a 1/10 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/185413 doi: bioRxiv preprint first posted online Sep. 7, 2017;