Description Usage Arguments Value Examples
RNASeq.Data is used to collect RNA-Seq data that need to be clustered.
1 | RNASeq.Data(Count, Normalizer=NULL, Treatment,GeneID=NULL)
|
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 |
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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ###### 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
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