clusterSeq-package {clusterSeq}R Documentation

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

Description

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

Details

The DESCRIPTION file:

Package: clusterSeq
Type: Package
Title: Clustering of high-throughput sequencing data by identifying co-expression patterns
Version: 1.14.0
Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils
Imports: BiocGenerics
Suggests: BiocStyle
Date: 2016-01-19
Author: Thomas J. Hardcastle & Irene Papatheodorou
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples.
License: GPL-3
LazyLoad: yes
biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression
git_url: https://git.bioconductor.org/packages/clusterSeq
git_branch: RELEASE_3_12
git_last_commit: f1e1602
git_last_commit_date: 2020-10-27
Date/Publication: 2020-10-27

Index of help topics:

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-package      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.

Author(s)

Thomas J. Hardcastle & Irene Papatheodorou

Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>

Examples

#Load in the processed data of observed read counts at each gene for each sample. 
data(ratThymus, package = "clusterSeq")

# Library scaling factors are acquired here using the getLibsizes
# function from the baySeq package.
libsizes <- getLibsizes(data = ratThymus)

# Adjust the data to remove zeros and rescale by the library scaling
# factors. Convert to log scale.
ratThymus[ratThymus == 0] <- 1
normRT <- log2(t(t(ratThymus / libsizes)) * mean(libsizes))

# run kCluster on reduced set.
normRT <- normRT[1:1000,]
kClust <- kCluster(normRT)

# make the clusters from these data.
mkClust <- makeClusters(kClust, normRT, threshold = 1)

# or using likelihood data from a Bayesian analysis of the data

# load in analysed countData object
data(cD.ratThymus, package = "clusterSeq")

# estimate likelihoods of dissimilarity on reduced set
aM <- associatePosteriors(cD.ratThymus[1:1000,])

# make clusters from dissimilarity data
sX <- makeClusters(aM, cD.ratThymus, threshold = 0.5)

# plot first six clusters
par(mfrow = c(2,3))
plotCluster(sX[1:6], cD.ratThymus)


[Package clusterSeq version 1.14.0 Index]