R: Clustering of high-throughput sequencing data by identifying co-expression patterns

# Clustering of high-throughput sequencing data by identifying co-expression patterns

## Documentation for package ‘clusterSeq’ version 1.36.0

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

clusterSeq-package | Clustering of high-throughput sequencing data by identifying co-expression patterns
associatePosteriors | Associates posterior likelihood to generate co-expression dissimilarities between genes
cD.ratThymus | Data from female rat thymus tissue taken from the Rat BodyMap project (Yu et al, 2014) and processed by baySeq.
clusterSeq | Clustering of high-throughput sequencing data by identifying co-expression patterns
kCluster | Constructs co-expression dissimilarities from k-means analyses.
makeClusters | Creates clusters from a co-expression minimal linkage data.frame.
makeClustersFF | Creates clusters from a file containing a full dissimilarity matrix.
plotCluster | Plots data from clusterings.
ratThymus | Data from female rat thymus tissue taken from the Rat BodyMap project (Yu et al, 2014).
wallace | Computes Wallace scores comparing two clustering methods.