PT  - JOURNAL ARTICLE
AU  - Thomas J. Hardcastle
AU  - Irene Papatheodorou
TI  - clusterSeq: methods for identifying co-expression in high-throughput sequencing data
AID  - 10.1101/188581
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 188581
4099  - http://biorxiv.org/content/early/2017/09/13/188581.short
4100  - http://biorxiv.org/content/early/2017/09/13/188581.full
AB  - Summary: Identifying gene co-expression is a significant step in understanding functional relationships between genes. Existing methods primarily depend on analyses of correlation between pairs of genes; however, this neglects structural elements between experimental conditions. We present a novel approach to identifying clusters of co-expressed genes that incorporates these structures.Availability: The methods are released on Bioconductor as the clusterSeq package (https://bioconductor.org/packages/release/bioc/html/clusterSeq.html).Contact: tjh48@cam.ac.uk