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This is the fourth revision of MBttest.Rnw,DESCRIPTION,ucscannot,and GMRP.R files

y.tan authored on 31/05/2018 22:57:16
Showing 5 changed files

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@@ -2,7 +2,7 @@ Package: GMRP
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 Type: Package
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 Title: GWAS-based Mendelian Randomization and Path Analyses
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 Version: 1.8.1
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-Date: 2018-05-23
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+Date: 2018-05-31
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 Author: Yuan-De Tan
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 Maintainer: Yuan-De Tan <tanyuande@gmail.com>
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 Description: Perform Mendelian randomization analysis of multiple SNPs
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@@ -16,4 +16,4 @@ Suggests: BiocStyle, BiocGenerics, VariantAnnotation
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 LazyLoad: yes
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 biocViews: Sequencing, Regression, SNP
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 NeedsCompilation: no
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-PackageStatus: Deprecated
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+VignetteBuilder: knitr
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@@ -1,5 +1,5 @@
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 mktable <-
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-function(cdata,ddata,rt="beta",varname,LG=1,Pv=0.00000005,Pc=0.979,Pd=0.979){
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+function(cdata,ddata,rt="beta",varname,LG=1,Pv=5e-08,Pc=0.979,Pd=0.979){
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 try(
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  if(is.null(cdata))
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@@ -42,7 +42,7 @@ This function just need  data of "Predicted function" and "symbol", so the other
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 }
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 \examples{
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 data(SNP368annot.data)
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-SNP368<-DataFrame(SNP368annot.data)
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+SNP368<-as.data.frame(SNP368annot.data)
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 ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3)
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 ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3,method=2)
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 }
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@@ -39,8 +39,8 @@ dim(data1)
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 dim(data2)
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 colnames(data1) <- c("SNP", "var1", "var2", "var3", "var4")
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 colnames(data2) <- c("SNP", "var1", "var2", "var3", "var4", "V1", "V2", "V3")
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-data1<-DataFrame(data1)
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-data2<-DataFrame(data2)
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+data1<-as.data.frame(data1)
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+data2<-as.data.frame(data2)
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 data12 <- fmerge(fl1=data1, fl2=data2, ID1="SNP", ID2="SNP", A=".dat1", B=".dat2", method="No")
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 ###################################################
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@@ -182,14 +182,14 @@ varname=varname, LG=1, Pv=0.00000005, Pc=0.979, Pd=0.979)
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 dim(mybeta)
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 beta <- mybeta[,4:8]   #  standard beta table for path analysis
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 snp <- mybeta[,1:3]   #  snp data for annotation analysis
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-beta<-DataFrame(beta)
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+beta<-as.data.frame(beta)
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 head(beta)
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 ###################################################
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 ### load beta data: 256-264
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 ###################################################
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 data(beta.data)
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-beta.data<-DataFrame(beta.data)
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+beta.data<-as.data.frame(beta.data)
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 CAD <- beta.data$cad
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 LDL <- beta.data$ldl
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 HDL <- beta.data$hdl
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@@ -217,7 +217,7 @@ abline(lm(CAD~TC), col="red", lwd=2)
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 ### MR and Path Analysis: 296-300
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 ###################################################
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 data(beta.data)
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-mybeta <- DataFrame(beta.data)
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+mybeta <- as.data.frame(beta.data)
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 mod <- CAD~LDL+HDL+TG+TC
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 pathvalue <- path(betav=mybeta, model=mod, outcome="CAD")
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@@ -260,7 +260,7 @@ disease="CAD", R2D=0.536535,R2O=0.988243)
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 ###################################################
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 data(SNP358.data)
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-SNP358 <- DataFrame(SNP358.data)
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+SNP358 <- as.data.frame(SNP358.data)
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 head(SNP358)
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 ###################################################
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@@ -284,7 +284,7 @@ snpPositAnnot(SNPdata=SNP358,SNP_hg19="chr",main="A")
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 ###################################################
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 data(SNP368annot.data)
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-SNP368<-DataFrame(SNP368annot.data)
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+SNP368<-as.data.frame(SNP368annot.data)
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 SNP368[1:10, ]
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 ###################################################
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@@ -11,7 +11,6 @@
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 <<style, echo=FALSE, results=tex>>=
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 BiocStyle::latex(use.unsrturl=FALSE)
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 @
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-
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 \title{GWAS-based Mendelian Randomization Path Analysis}
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 \author{Yuan-De Tan \\
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 \texttt{tanyuande@gmail.com}}
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@@ -81,8 +80,8 @@ dim(data1)
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 dim(data2)
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 colnames(data1) <- c("SNP", "var1", "var2", "var3", "var4")
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 colnames(data2) <- c("SNP", "var1", "var2", "var3", "var4", "V1", "V2", "V3")
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-data1<-DataFrame(data1)
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-data2<-DataFrame(data2)
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+data1<-as.data.frame(data1)
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+data2<-as.data.frame(data2)
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 data12 <- fmerge(fl1=data1, fl2=data2, ID1="SNP", ID2="SNP", A=".dat1", B=".dat2", method="No")
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 @
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@@ -272,7 +271,7 @@ To roughly display relationship of the undefined causal variables to disease of
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 <<>>=
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 data(beta.data)
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-beta.data<-DataFrame(beta.data)
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+beta.data<-as.data.frame(beta.data)
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 CAD <- beta.data$cad
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 LDL <- beta.data$ldl
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 HDL <- beta.data$hdl
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@@ -307,7 +306,7 @@ After \textbf{standard beta table} was successfully created by \Rfunction{mktabl
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 <<path, keep.source=TRUE, eval=FALSE>>=
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 data(beta.data)
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-mybeta <- DataFrame(beta.data)
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+mybeta <- as.data.frame(beta.data)
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 mod <- CAD~LDL+HDL+TG+TC
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 pathvalue <- path(betav=mybeta, model=mod, outcome="CAD")
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 @