clusterSeq-package {clusterSeq} | R Documentation |
Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples.
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.
Thomas J. Hardcastle & Irene Papatheodorou
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
#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)