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Last data update: 2014.03.03

R: separately plot quantile-quantile plot for the return...

VS.Allele.QQ

R Documentation

separately plot quantile-quantile plot for the return value(allele count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study

Description

separately plot quantile-quantile plot for the return value(allele count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study.

Usage

VS.Allele.QQ(Trad_case_1, Trad_case_2, Trad_control_1, Trad_control_2, 
    MC_case_1, MC_case_2, MC_control_1, MC_control_2, 
	 scatter_col = "black", line_col = "black", 
	 main = "QQ plot for allele model", 
	 title = c("case_A", "case_a", "control_A", "control_a"), 
	 xlab = "Quantile of count (TradPerm)", ylab = "Quantile of count (MCPerm)")

Arguments

`Trad_case_1`	a numeric vector, simulative allele 1 count for case samples got by TradPerm method for certain study.
`Trad_case_2`	a numeric vector, simulative allele 2 count for case samples got by TradPerm method for certain study.
`Trad_control_1`	a numeric vector, simulative allele 1 count for control samples got by TradPerm method for certain study.
`Trad_control_2`	a numeric vector, simulative allele 2 count for control samples got by TradPerm method for certain study.
`MC_case_1`	a numeric vector, simulative allele 1 count for case samples got by MCPerm method for certain study.
`MC_case_2`	a numeric vector, simulative allele 2 count for case samples got by MCPerm method for certain study.
`MC_control_1`	a numeric vector, simulative allele 1 count for control samples got by MCPerm method for certain study.
`MC_control_2`	a numeric vector, simulative allele 2 count for control samples got by MCPerm method for certain study.
`scatter_col`	the color for scatter points of quantile-quantile plot. Default value is 'black'.
`line_col`	the color of line which passes through the sample distribution probs quantiles, the first and third quartiles. Default value is 'black'.
`main`	the main title(on top). Default value is "QQ plot for allele model".
`title`	the sub main title for each plot(on top). Default value is a vector with elements: 'case_A', 'case_a', 'control_A' and 'control_a'.
`xlab,ylab`	X axis label, default value is "Quantile of count (TradPerm)". Y axis label, default value is "Quantile of count (MCPerm)".

Details

Separately plotting quantile-quantile plot for the return value(allele count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study is to compare the simulative allele count distribution got by TradPerm and MCPerm method whether are same.

MCPerm details see chisq.MCPerm. TradPerm details see chisq.TradPerm.

Author(s)

Lanying Zhang and Yongshuai Jiang <jiangyongshuai@gmail.com>

References

William S Noble(Nat Biotechnol.2009): How does multiple testing correction work?

Edgington. E.S.(1995): Randomization tests, 3rd ed.

Examples

## import data
# data(MetaGenotypeData)
## delete first line which contains the names of each column
# temp=MetaGenotypeData[-1,];
# rowNum=nrow(temp)
# gen=matrix(0,nrow=rowNum,ncol=1);
# aff=matrix(0,nrow=rowNum,ncol=1);
# for(j in 1:rowNum){
	 # gen[j,]=paste(temp[j,14],temp[j,15],sep=" ");
	 # case_num=length(unlist(strsplit(temp[j,14],split=" ")));
	 # control_num=length(unlist(strsplit(temp[j,15],split=" ")));
	 # case_aff=paste(rep(2,case_num),collapse=" ");
	 # control_aff=paste(rep(1,control_num),collapse=" ");
	 # aff[j,]=paste(case_aff,control_aff,sep=" ");
# }
# result1=meta.TradPerm(gen,aff,split=" ",sep="/",naString="-",
    # model="allele",method="MH",repeatNum=1000) 
# result1
## plot study 12
# Trad_case_1=2*result1$perm_case_11[12,]+result1$perm_case_12[12,]
# Trad_case_2=2*result1$perm_case_22[12,]+result1$perm_case_12[12,]
# Trad_control_1=2*result1$perm_control_11[12,]+result1$perm_control_12[12,]
# Trad_control_2=2*result1$perm_control_22[12,]+result1$perm_control_12[12,]

## import data
# data(MetaGenotypeCount)
## delete the first line which is the names for columns.
# temp=MetaGenotypeCount[-1,,drop=FALSE]
# result=meta.MCPerm(case_11=as.numeric(temp[,14]),case_12=as.numeric(temp[,16]),
	 # case_22=as.numeric(temp[,18]),control_11=as.numeric(temp[,15]),
	 # control_12=as.numeric(temp[,17]),control_22=as.numeric(temp[,19]),
	 # model="allele",method="MH",repeatNum=100000)
# result2
## plot study 12
# MC_case_1=2*result2$perm_case_11[12,]+result2$perm_case_12[12,]
# MC_case_2=2*result2$perm_case_22[12,]+result2$perm_case_12[12,]
# MC_control_1=2*result2$perm_control_11[12,]+result2$perm_control_12[12,]
# MC_control_2=2*result2$perm_control_22[12,]+result2$perm_control_12[12,]

# VS.Allele.QQ(Trad_case_1,Trad_case_2,Trad_control_1,Trad_control_2,
    # MC_case_1,MC_case_2,MC_control_1,MC_control_2,
    # main="cumulative distribution curve for allele model",
	 # title=c("case_A","case_a","control_A","control_a"))