Cluster.RNASeq: Do clustering for count data based on poisson or...

In MBCluster.Seq: Model-Based Clustering for RNA-seq Data

Description

Given a set of initial cluster centers and specify the iteration algorithm, the function proceed the model-based clustering.

Usage

Cluster.RNASeq(data, model, centers = NULL, method = c("EM", "DA", "SA"), 
  iter.max = 30, TMP = NULL)

Arguments

data	RNA-seq data from output of function RNASeq.Data()
model	Currently could be either Poisson or negative-binomial model for count data
centers	Initial cluster centers as a matrix of K rows and I columns to start the clustering algorithm. Each rows is mean-centered to have zero sum. A recommended initial set can be obtained by KmeansPlus.RNASeq()
method	Iteration algorithm to update the estimates of cluster and their centers. Could be Expectation-Maximization (EM), Deterministic Annealing (DA) or Simulated Annealing (SA).
iter.max	The maximum number of iterations allowed
TMP	The 'temperature' serving as annealing rate for DA and SA algorithms. The default setting starts from TMP=4 with decreasing rate 0.9

Value

probability	a matrix containing the probability of each gene belonging to each cluster
centers	estimates of the cluster centers, a matrix with the same dimension as the initial input
cluster	a vector taking values between 1,2,...,K, indicating the assignments of the objects to the clusters

References

Model-Based Clustering for RNA-seq Data, Yaqing Si , Peng Liu, Pinghua Li and Thomas Brutnell

Examples

###### run the following codes in order
#
# data("Count")     ## a sample data set with RNA-seq expressions 
#                   ## for 1000 genes, 4 treatment and 2 replicates
# head(Count)
# GeneID=1:nrow(Count)
# Normalizer=rep(1,ncol(Count))
# Treatment=rep(1:4,2)
# mydata=RNASeq.Data(Count,Normalize=NULL,Treatment,GeneID) 
#                   ## standardized RNA-seq data
# c0=KmeansPlus.RNASeq(mydata,nK=10)$centers
#                   ## choose 10 cluster centers to initialize the clustering 
# cls=Cluster.RNASeq(data=mydata,model="nbinom",centers=c0,method="EM")$cluster
#                   ## use EM algorithm to cluster genes
# tr=Hybrid.Tree(data=mydata,cluste=cls,model="nbinom")
#                   ## bulild a tree structure for the resulting 10 clusters
# plotHybrid.Tree(merge=tr,cluster=cls,logFC=mydata$logFC,tree.title=NULL)
#                   ## plot the tree structure

Example output

MBCluster.Seq documentation built on May 2, 2019, 9:22 a.m.