snpcor.best: Subestting for highest correlations

In 16xchen/Biomy: Novel tool to find genetic factors that drive a set of traits

Description

Subsets dataframe from snpcor function to contain only the highly correlated SNP dendrograms

Usage

snpcor.best(cordata, threshold)

Arguments

cordata	q dataframe generated from the function snpcor
threshold	percent threshold (0-1) to search for highest correlations, default is 0.95

Value

a subsetted correlation dataframe

Author(s)

Xingyao Chen

Examples

set.seed(1234) #simulate quantitative trait dataframe
x <- rnorm(n=(16*12), mean=10, sd=30)
trait=matrix(x,16,12)
rname=paste("trait",1:16,sep="")
cname=paste("strain", 1:12, sep="")
rownames(trait)=rname
colnames(trait)=cname

mytrait.dend=traittree(traitdata=trait, nboot=10) #cluster strains by traitbiome

chrX = SampSNP()  #load SNP data
strain.names=colnames(trait) #assign the same strain names
mysnptree=maketree(snpdata=chrX, strain.names=strain.names) #cluster strains by every 100 SNPs

mycordata=snpcor(snpdata=chrX, snptree=mysnptree, trait.dend=mytrait.dend) #calculate correlation coefficient for micribiome dendrogram and each SNP dendrogram
mycordata.best=snpcor.best(cordata=mycordata,threshold = 0.95) #find highly correlated SNP dendrograms

16xchen/Biomy documentation built on May 5, 2019, 10:39 a.m.