Trendy – segmented regression analysis of expression dynamics in 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 studying 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.

University of Florida researchers 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. The researchers demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-sequencing (RNA-seq) datasets.

Trendy Framework

rna-seq

The Trendy framework fits multiple segmented regression models to each feature/gene. The optimal model is selected as the one with the smallest BIC. Trendy summarizes the expression pattern of each gene and provides a summary of global dynamics

Availability – Trendy is freely available on Bioconductor with a full vignette at: https://bioconductor.org/packages/release/bioc/html/Trendy.html

Bacher R, Leng N, Chu LF, Ni Z, Thomson JA, Kendziorski C, Stewart R. (2018) Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 19(1):380. [article]

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