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Last data update: 2014.03.03

R: Determine clusters of individuals

snpgdsCutTree

R Documentation

Determine clusters of individuals

Description

To determine sub groups of individuals using a specified dendrogram from hierarchical cluster analysis

Usage

snpgdsCutTree(hc, z.threshold=15, outlier.n=5, n.perm = 5000, samp.group=NULL,
    col.outlier="red", col.list=NULL, pch.outlier=4, pch.list=NULL,
    label.H=FALSE, label.Z=TRUE, verbose=TRUE)

Arguments

`hc`	an object of `snpgdsHCluster`
`z.threshold`	the threshold of Z score to determine whether split the node or not
`outlier.n`	the cluster with size less than or equal to `outlier.n` is considered as outliers
`n.perm`	the times for permutation
`samp.group`	if `NULL`, determine groups by Z score; if a vector of factor, assign each individual in dendrogram with respect to `samp.group`
`col.outlier`	the color of outlier
`col.list`	the list of colors for different clusters
`pch.outlier`	plotting 'character' for outliers
`pch.list`	plotting 'character' for different clusters
`label.H`	if TRUE, plotting heights in a dendrogram
`label.Z`	if TRUE, plotting Z scores in a dendrogram
`verbose`	if TRUE, show information

Details

The details will be described in future.

Value

Return a list:

`sample.id`	the sample ids used in the analysis
`z.threshold`	the threshold of Z score to determine whether split the node or not
`outlier.n`	the cluster with size less than or equal to `outlier.n` is considered as outliers
`samp.order`	the order of samples in the dendrogram
`samp.group`	a vector of factor, indicating the group of each individual
`dmat`	a matrix of pairwise group dissimilarity
`dendrogram`	the dendrogram of individuals
`merge`	a data.frame of `(z, n1, n2)` describing each combination: `z`, the Z score; `n1`, the size of the first cluster; `n2`, the size of the second cluster
`clust.count`	the counts for clusters

Author(s)

Xiuwen Zheng

Examples

# open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())

pop.group <- as.factor(read.gdsn(index.gdsn(
    genofile, "sample.annot/pop.group")))
pop.level <- levels(pop.group)

diss <- snpgdsDiss(genofile)
hc <- snpgdsHCluster(diss)

# close the genotype file
snpgdsClose(genofile)



###################################################################
# cluster individuals
#

set.seed(100)
rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)

# the distribution of Z scores
snpgdsDrawTree(rv, type="z-score", main="HapMap Phase II")

# draw dendrogram
snpgdsDrawTree(rv, main="HapMap Phase II",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))


###################################################################
# or cluster individuals by ethnic information
#

rv2 <- snpgdsCutTree(hc, samp.group=pop.group)

# cluster individuals by Z score, specifying 'clust.count'
snpgdsDrawTree(rv2, rv$clust.count, main="HapMap Phase II",
    edgePar = list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    labels = c("YRI", "CHB/JPT", "CEU"), y.label=0.1)
legend("bottomleft", legend=levels(pop.group), col=1:nlevels(pop.group),
    pch=19, ncol=4, bg="white")



###################################################################
# zoom in ...
#

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(1),
    main="HapMap Phase II -- YRI",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,2),
    main="HapMap Phase II -- CEU",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,1),
    main="HapMap Phase II -- CHB/JPT",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(SNPRelate)
Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SNPRelate/snpgdsCutTree.Rd_%03d_medium.png", width=480, height=480)
> ### Name: snpgdsCutTree
> ### Title: Determine clusters of individuals
> ### Aliases: snpgdsCutTree
> ### Keywords: GDS GWAS
> 
> ### ** Examples
> 
> # open an example dataset (HapMap)
> genofile <- snpgdsOpen(snpgdsExampleFileName())
> 
> pop.group <- as.factor(read.gdsn(index.gdsn(
+     genofile, "sample.annot/pop.group")))
> pop.level <- levels(pop.group)
> 
> diss <- snpgdsDiss(genofile)
Individual dissimilarity analysis on SNP genotypes:
Excluding 365 SNPs on non-autosomes
Excluding 1 SNP (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 279 samples, 8722 SNPs
	using 1 (CPU) core
Dissimilarity:	the sum of all selected genotypes (0, 1 and 2) = 2446510
Dissimilarity:	Wed Jul  6 05:34:32 2016	0%
Dissimilarity:	Wed Jul  6 05:34:33 2016	100%
> hc <- snpgdsHCluster(diss)
> 
> # close the genotype file
> snpgdsClose(genofile)
> 
> 
> 
> ###################################################################
> # cluster individuals
> #
> 
> set.seed(100)
> rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)
Determine groups by permutation (Z threshold: 15, outlier threshold: 5):
Create 3 groups.
> 
> # the distribution of Z scores
> snpgdsDrawTree(rv, type="z-score", main="HapMap Phase II")
> 
> # draw dendrogram
> snpgdsDrawTree(rv, main="HapMap Phase II",
+     edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))
> 
> 
> ###################################################################
> # or cluster individuals by ethnic information
> #
> 
> rv2 <- snpgdsCutTree(hc, samp.group=pop.group)
Create 4 groups.
> 
> # cluster individuals by Z score, specifying 'clust.count'
> snpgdsDrawTree(rv2, rv$clust.count, main="HapMap Phase II",
+     edgePar = list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
+     labels = c("YRI", "CHB/JPT", "CEU"), y.label=0.1)
> legend("bottomleft", legend=levels(pop.group), col=1:nlevels(pop.group),
+     pch=19, ncol=4, bg="white")
> 
> 
> 
> ###################################################################
> # zoom in ...
> #
> 
> snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(1),
+     main="HapMap Phase II -- YRI",
+     edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
+     y.label.kinship=TRUE)
> 
> snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,2),
+     main="HapMap Phase II -- CEU",
+     edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
+     y.label.kinship=TRUE)
> 
> snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,1),
+     main="HapMap Phase II -- CHB/JPT",
+     edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
+     y.label.kinship=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>