clusterSeq-package: Clustering of high-throughput sequencing data by identifying...

In tjh48/clusterSeq: 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

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Author(s)

Thomas J. Hardcastle & Irene Papatheodorou

Maintainer: Thomas J. Hardcastle <[e-mail]>

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)

tjh48/clusterSeq documentation built on May 31, 2019, 3:40 p.m.