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: | 1.4.0
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

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 1.4.0 Index]