VS.Genotype.QQ: separately plot quantile-quantile plot for the return...

In MCPerm: A Monte Carlo permutation method for multiple test correlation

Description

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

Usage

VS.Genotype.QQ(Trad_case_11, Trad_case_12, Trad_case_22, 
    Trad_control_11, Trad_control_12, Trad_control_22, 
	 MC_case_11, MC_case_12, MC_case_22, 
	 MC_control_11, MC_control_12, MC_control_22,
	 scatter_col = "black", line_col = "black", 
	 title = NULL, xlab = "Quantile of genotype count (TradPerm)", 
	 ylab = "Quantile of genotype count (MCPerm)")

Arguments

Trad_case_11	a numeric vector, the return value 'perm_case_11' got by meta.TradPerm method for certain study.
Trad_case_12	a numeric vector, the return value 'perm_case_12' got by meta.TradPerm method for certain study.
Trad_case_22	a numeric vector, the return value 'perm_case_22' got by meta.TradPerm method for certain study.
Trad_control_11	a numeric vector, the return value 'perm_control_11' got by meta.TradPerm method for certain study.
Trad_control_12	a numeric vector, the return value 'perm_control_12' got by meta.TradPerm method for certain study.
Trad_control_22	a numeric vector, the return value 'perm_control_22' got by meta.TradPerm method for certain study.
MC_case_11	a numeric vector, the return value 'perm_case_11' got by meta.MCPerm method for certain study.
MC_case_12	a numeric vector, the return value 'perm_case_12' got by meta.MCPerm method for certain study.
MC_case_22	a numeric vector, the return value 'perm_case_22' got by meta.MCPerm method for certain study.
MC_control_11	a numeric vector, the return value 'perm_control_11' got by meta.MCPerm method for certain study.
MC_control_12	a numeric vector, the return value 'perm_control_12' got by meta.MCPerm method for certain study.
MC_control_22	a numeric vector, the return value 'perm_control_22' got by meta.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'.
title	the main title(on top).
xlab,ylab	X axis label, default value is "Quantile of genotype count (TradPerm)". Y axis label, default value is "Quantile of genotype count (MCPerm)".

Details

Separately plotting quantile-quantile plot for the return value(genotype 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 <[e-mail]>

References

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

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

See Also

meta.MCPerm, meta.TradPerm, chisq.MCPerm, chisq.TradPerm, VS.QQ, VS.KS, VS.Genotype.CDC, VS.Genotype.Hist, VS.Allele.QQ, PermMeta.LnOR.qqnorm

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

## 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
# VS.Genotype.CDC(result1$perm_case_11[12,],result1$perm_case_12[12,],result1$perm_case_22[12,],
    # result1$perm_control_11[12,],result1$perm_control_12[12,],result1$perm_control_22[12,],
	 # result2$perm_case_11[12,],result2$perm_case_12[12,],result2$perm_case_22[12,],
	 # result2$perm_control_11[12,],result2$perm_control_12[12,],result2$perm_control_22[12,],
    # Trad_col="grey",MC_col="black", title="hist_plot for six genotype")

MCPerm documentation built on May 29, 2017, 11:27 a.m.