Package: clusterSeq 1.29.0

Samuel Granjeaud

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

Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples.

Authors:Thomas J. Hardcastle [aut], Irene Papatheodorou [aut], Samuel Granjeaud [cre]

# Install 'clusterSeq' in R:

install.packages('clusterSeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

cD.ratThymus - Data from female rat thymus tissue taken from the Rat BodyMap project (Yu et al, 2014) and processed by baySeq.
ratThymus - Data from female rat thymus tissue taken from the Rat BodyMap project (Yu et al, 2014).

On BioConductor:clusterSeq-1.29.0(bioc 3.20)clusterSeq-1.28.0(bioc 3.19)

Last updated 2 months agofrom:cfc065b305

Last update: 2018-08-30
Started: 2016-12-12

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