Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Rhonda Bacher, Ning Leng, Li-Fang Chu, Zijian Ni, James A. Thomson, Christina Kendziorski, Ron Stewart

ABSTRACT

BACKGROUND: 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. RESULTS: 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-sequencing (RNA-seq) datasets. CONCLUSIONS: 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 on Bioconductor with a full vignette at https://bioconductor.org/packages/release/bioc/html/Trendy.html . More... »

PAGES

380

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-018-2405-x

DOI

http://dx.doi.org/10.1186/s12859-018-2405-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107668200

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30326833


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