Find names of leaves belongigng to given branches of a hierarchical tree

Description

Description: find the names of all items comprising one or more branches of a hierarchical tree.

Usage

LeafContent(myinput, mynode=NA)

Arguments

Value

A list of items, of the same length as mynode. Each item corresponds to a branch listed in myneode and is a character vector containing the names of the leaves in the branch.

Author(s)

Guoli Sun, Alex Krasnitz

Examples

data(leukemia)
hc<-hclust(dist(data.matrix(leukemia)),"ward")
#find the name of leaf 29
LeafContent(hc,mynode=c(-29))
#find the name of leaf 29 and leaves belonging to node 29
LeafContent(hc,mynode=c(-29,29))
## Not run: 
mytable<-SigTree(data.matrix(leukemia),mystat="fldc",
        mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
        distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
LeafContent(mytable,mynode=c(-29,29))
mypartition<-PartitionTree(x=mytable,siglevel=0.001,statname="fldc",sigtype="raw")
LeafContent(mypartition)

## End(Not run)

Find the most detailed partition of a tree into tight branches.

Description

Description: The function finds the most detailed partition of a hierartchical tree into tight branches, given a level of significance for tightness.

Usage

PartitionTree(x,siglevel=0.05,statname="fldc",
        sigtype=c("raw","corrected","fdr"))

Arguments

Value

An object of class partition. See ?partition for details.

Author(s)

Guoli Sun, Alex Krasnitz

See Also

SigTree,partition,best

Examples

## Not run: 
data(leukemia)
mytable<-SigTree(data.matrix(leukemia),mystat="all",
        mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
        distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
mypartition<-PartitionTree(x=mytable,siglevel=0.001,statname="fldc",
        sigtype="raw")
partition1<-mypartition$partition
sigmatrix1<-mypartition$sigvalue
fix(partition1)
fix(sigmatrix1)

## End(Not run)

Perform statistical analysis of tightness for branches of a hierarchical cluster.

Description

Description: Given data from which a hierarchical tree is grown, compute measures of tightness for each branch, sample from the null distribution of these measures in the randomized data and compute the corresponding p-values.

Usage

SigTree(myinput,mystat=c("all","fldc","bldc","fldcc","slb"),
        mymethod="complete",mymetric="euclidean",rand.fun=NA,
        by.block=NA,distrib=c("vanilla","Rparallel"),Ptail=TRUE,
        tailmethod=c("ML","MOM"),njobs=1,seed=NA,
        Nperm=ifelse(Ptail,1000,1000*nrow(myinput)),
        metric.args=list(),rand.args=list())

Arguments

Details

When rand.fun is set to the name of a user supplied randomization function, the first argument of that function should be set to myinput. See examples below.

The measures of tightness are defined as follows. Denote a node in the tree by a, its sibling node by b, and their parent node by p. Let their respective geights be ha,hb,hp. Finally, let Sx mean that the measure S is computed for the node x. Then the definitions are

fldc:

Sa = (hp-ha)/hp

fldcc:

Sa = (hp-(ha-hb)/2)/ha

bldc:

Sp = (2*hp-ha-hb)/(2*hp)

slb:

Sp = 2*hp-ha-hb

The first three measures test tightnss of all internal nodes at the same time, while slb only tests two-way split of input data. The seed argument is optional. Setting the seed ensures reproducibility of sampling from the null distribution.

Value

If rand.fun is set to NA, the function returns a matrix whose rows correspond to the internal nodes of the tree and whose columns contain the tree structure as in the merge component of the class hclust; the height component of hclust;and columns tabulating the values of the measures of tightness specified by the mystat argument. If rand.fun is set to a specific randomization method, an object of class best is returned. See ?best for details.

Note

If mymetric or rand.fun is a customized function, make sure you have read and write permission for your working directory.

Author(s)

Guoli Sun, Alex Krasnitz

References

Theo A. Knijnenburg, Lodewyk F. A. Wessels et al (2009) Fewer permutations, more accurate P-values

See Also

best,plot.best

Examples

####Each column is a gene expression profile for a case of leukemia. 
####Each case belongs to one of three subtypes.
data(leukemia)
#output only statistic table
mytable<-SigTree(data.matrix(leukemia),mystat="all",
        mymethod="ward",mymetric="euclidean")
class(mytable)
## Not run: 
#use multicore processing to detect significant sub-clusters
mytable<-SigTree(data.matrix(leukemia),mystat="all",
	mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
	distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
class(mytable)
####Each row after the 1st describes an item belonging to one of four subtypes. 
####Each column corresponds to a genomic location in one of 22 human chromosomes. 
####The 1st row contains the chromosome numbers.
data(T10)
#Perform randomization within each chromosome
chrom<-as.numeric(T10[1,])
mydata<-T10[-1,] 
mytable<-SigTree(data.matrix(mydata),mystat="fldc",        
	mymethod="ward",mymetric="euclidean",rand.fun="shuffle.block",
	by.block=chrom,distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
#Compute dissimilarity using a user-supplied distance function,
#and perform randomization using a user-supplied randomization function, 
#with additional arguments. 
#Both user-supplied functions are only useful as illustration.
mydist<-function(x,y){return(dist(x)/y)}
myrand<-function(x,z){return(apply(x+z,2,sample))}
mytable<-SigTree(data.matrix(leukemia),mystat="fldc",
mymethod="ward",mymetric="mydist",rand.fun="myrand",
distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="MOM",metric.args=list(3),
rand.args=list(2))

## End(Not run)

Breast tumor single cells data

Description

This data set summarizes DNA copy number variation in 100 individual cancer cells harvested from a breast tumor. The cells belong to four subtypes, differing by ploidy.There are 47 Diploid+Pseudo-diploid, 24 Hypo-diploid, 4 Aneuploid B and 25 Aneuploid A cells. Their copy number profiles are summarized in terms of 354 amplification and deletion "cores", are computed by the CORE package.

Usage

data(T10)

Format

A data frame with 101 rows and 354 columns. Each column corresponds to a core. The first row is integer and contains the chromosome number for each core. The remaining rows are numeric, with values between 0 and 1, and each represents a DNA copy number profile of a cell.

Details

Please remove the first row before computing the distance matrix.

Source

Alexander Krasnitz, Guoli Sun, Peter Andrews, and Michael Wigler(2013) Target inference from collections of genomic intervals

References

Alexander Krasnitz, Guoli Sun, Peter Andrews, and Michael Wigler(2013) Target inference from collections of genomic intervals

Examples

data(T10)
dim(T10)

An object of class "best"

Description

Description: This object is a list of three items. It contains a statistical assessment of the tightness of branches in a hierarchical tree.

Value

Author(s)

Guoli Sun, Alex Krasnitz

See Also

SigTree,plot.best

Examples

## Not run: 
data(leukemia)
mytable<-SigTree(data.matrix(leukemia),mystat="all",
       mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
       distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
class(mytable)
names(mytable)
mytable<-SigTree(data.matrix(leukemia),mystat="slb",
       mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
       distrib="Rparallel",njobs=2,Ptail=FALSE)
class(mytable)
names(mytable)

## End(Not run)

Leukemia data

Description

This data set represents mRNA expression of 500 genes in 38 patient cases of leukemia. These 38 cases fall into 3 subtypes: AML (11), T-lineage ALL (8) and B-lineage ALL (19). The set was obtained by removing 499 genes from Golub's leukemia data, to facilitate the execution of examples for this package.

Usage

data(leukemia)

Format

A data frame with 38 observations (rows) of 500 variables (columns).

Details

Bone marrow samples obtained from acute leukemia patients at the time of diagnosis.

Source

http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi

References

T.R. Golub, D.K. Slonim et al(1999) Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression;

Stefano Monti, Pablo Tamayo, Jill Mesirov, and Todd Golub(2003) Consensus Clustering: A resampling-based method for class discovery and visualization of gene expression microarray data

Examples

data(leukemia)
dim(leukemia)

An object of class "partition"

Description

Description: This object is a list of four items, which jointly specify a detailed partition of a hierarchical tree into tight branches.

Value

Author(s)

Guoli Sun, Alex Krasnitz

See Also

PartitionTree,best,SigTree

Examples

## Not run: 
data(leukemia)
mytable<-SigTree(data.matrix(leukemia),mystat="all",
       mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
       distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
class(mytable)
mypartition<-PartitionTree(x=mytable,siglevel=0.001,statname="fldc",
       sigtype="raw")
class(mypartition)
names(mypartition)

## End(Not run)

Plot a dendrogam of a hierarchical cluster with branches labeled by their numbers and significance estimates of tightness.

Description

Description: A plot method for the class best.

Usage

## S3 method for class 'best'
plot(x,mystat="fldc",siglevel=0.05,sigtype=c("raw","corrected","fdr"),
        partition=NA,print.num=TRUE,print.lab=TRUE,float=0.01,col.best=c(2,3),
        cex.best=0.8,cex.leaf=0.8,font.best=NULL,main=NULL,sub=NULL,xlab=NULL,
        metric.args=list(),...)

Arguments

Details

The function plots a dendrogram of the hierarchical tree as specified by the x argument, an object of class "best". When argument partition is set to an object of class "partition", and a partition does exist (see partition for description), this plot provides the significance estimates for the nodes that form the partition. Otherwise, this function puts legends on all tight nodes with significance estimates no more than siglevel. To obtain the leaves descending from a given node, refer to function LeafContent.

Value

A plot with all branch numbers and significant pvalues in the hierarchical tree.

Author(s)

Guoli Sun, Alex Krasnitz

See Also

SigTree, PartitionTree,best,partition

Examples

## Not run: 
data(leukemia)
mytable<-SigTree(data.matrix(leukemia),mystat="all",
        mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
        distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
plot(x=mytable,mystat="fldc",siglevel=0.001,sigtype="raw",hang=-1)
mypartition<-PartitionTree(x=mytable,siglevel=0.001,statname="fldc",
        sigtype="raw")
plot(x=mytable,mystat="fldc",partition=mypartition)
plot(x=mytable,mystat="fldc",partition=mypartition,print.num=F)
#with user-defined functions
mydist<-function(x,y){return(dist(x)/y)}
myrand<-function(x,z){return(apply(x+z,2,sample))}
mytable<-SigTree(data.matrix(leukemia),mystat="fldc",
	mymethod="ward",mymetric="mydist",rand.fun="myrand",
	distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="MOM",metric.args=list(3),
	rand.args=list(2))
plot(mytable,metric.args=list(3))
plot(mytable,metric.args=list(3),cex.leaf=1.5)

## End(Not run)