MBCluster.Seq (version 1.0)

RNASeq.Data: Standardize RNASeq Data for Clustering

Description

RNASeq.Data is used to collect RNA-Seq data that need to be clustered.

Usage

RNASeq.Data(Count, Normalizer=NULL, Treatment,GeneID=NULL)

Arguments

Count

a GxP matrix storing the numbers of reads mapped to G genes in P samples. Non-integer values are allowed.

Normalizer

a vector of length P or a GxP matrix to normalize the gene expressions. When Normalizer=NULL, we use log(Q2) by default, where Q3 is the 75

Treatment

a vector of length P indicating the assignment of treatments for each column of the Count. For example, Treatment=c(1,1,2,2,3,3) means there are 3 treatments with each having 2 replicates

GeneID

the ID's of the genes, labeled by 1,2,...,G if not provided

Value

GeneID

ID's of genes provided by the user. Default is 1,2,...,G if not provided

Treatment

The same as the input, but is sorted in increasing order.

Count

The matrix of counts of reads as provided. The columns of the matrix is re-arranged to match the ordered labels of treatment

Normalizer

A matrix contains the input normalization factors as provided or from default setting. If the provided value is a vector, then each column of the matrix will have the same value

logFC

A matrix contains the log fold change (log-FC) of the normalized genes expressions across all the treatments. Each row of the log-FC matrix is standardized to has zero sum

Aver.Expr

the logarithm of the mean gene expression after normalization

logFC

a matrix storing the gene profiles, which is defined as the log fold changes relative to the mean gene expression

NB.Dispersion

the estimated gene-wise dispersion if assuming NB model

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