| ... | ... |
@@ -2,7 +2,7 @@ Package: GMRP |
| 2 | 2 |
Type: Package |
| 3 | 3 |
Title: GWAS-based Mendelian Randomization and Path Analyses |
| 4 | 4 |
Version: 1.8.1 |
| 5 |
-Date: 2018-05-23 |
|
| 5 |
+Date: 2018-05-31 |
|
| 6 | 6 |
Author: Yuan-De Tan |
| 7 | 7 |
Maintainer: Yuan-De Tan <tanyuande@gmail.com> |
| 8 | 8 |
Description: Perform Mendelian randomization analysis of multiple SNPs |
| ... | ... |
@@ -16,4 +16,4 @@ Suggests: BiocStyle, BiocGenerics, VariantAnnotation |
| 16 | 16 |
LazyLoad: yes |
| 17 | 17 |
biocViews: Sequencing, Regression, SNP |
| 18 | 18 |
NeedsCompilation: no |
| 19 |
-PackageStatus: Deprecated |
|
| 19 |
+VignetteBuilder: knitr |
| ... | ... |
@@ -42,7 +42,7 @@ This function just need data of "Predicted function" and "symbol", so the other |
| 42 | 42 |
} |
| 43 | 43 |
\examples{
|
| 44 | 44 |
data(SNP368annot.data) |
| 45 |
-SNP368<-DataFrame(SNP368annot.data) |
|
| 45 |
+SNP368<-as.data.frame(SNP368annot.data) |
|
| 46 | 46 |
ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3) |
| 47 | 47 |
ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3,method=2) |
| 48 | 48 |
} |
| ... | ... |
@@ -39,8 +39,8 @@ dim(data1) |
| 39 | 39 |
dim(data2) |
| 40 | 40 |
colnames(data1) <- c("SNP", "var1", "var2", "var3", "var4")
|
| 41 | 41 |
colnames(data2) <- c("SNP", "var1", "var2", "var3", "var4", "V1", "V2", "V3")
|
| 42 |
-data1<-DataFrame(data1) |
|
| 43 |
-data2<-DataFrame(data2) |
|
| 42 |
+data1<-as.data.frame(data1) |
|
| 43 |
+data2<-as.data.frame(data2) |
|
| 44 | 44 |
data12 <- fmerge(fl1=data1, fl2=data2, ID1="SNP", ID2="SNP", A=".dat1", B=".dat2", method="No") |
| 45 | 45 |
|
| 46 | 46 |
################################################### |
| ... | ... |
@@ -182,14 +182,14 @@ varname=varname, LG=1, Pv=0.00000005, Pc=0.979, Pd=0.979) |
| 182 | 182 |
dim(mybeta) |
| 183 | 183 |
beta <- mybeta[,4:8] # standard beta table for path analysis |
| 184 | 184 |
snp <- mybeta[,1:3] # snp data for annotation analysis |
| 185 |
-beta<-DataFrame(beta) |
|
| 185 |
+beta<-as.data.frame(beta) |
|
| 186 | 186 |
head(beta) |
| 187 | 187 |
|
| 188 | 188 |
################################################### |
| 189 | 189 |
### load beta data: 256-264 |
| 190 | 190 |
################################################### |
| 191 | 191 |
data(beta.data) |
| 192 |
-beta.data<-DataFrame(beta.data) |
|
| 192 |
+beta.data<-as.data.frame(beta.data) |
|
| 193 | 193 |
CAD <- beta.data$cad |
| 194 | 194 |
LDL <- beta.data$ldl |
| 195 | 195 |
HDL <- beta.data$hdl |
| ... | ... |
@@ -217,7 +217,7 @@ abline(lm(CAD~TC), col="red", lwd=2) |
| 217 | 217 |
### MR and Path Analysis: 296-300 |
| 218 | 218 |
################################################### |
| 219 | 219 |
data(beta.data) |
| 220 |
-mybeta <- DataFrame(beta.data) |
|
| 220 |
+mybeta <- as.data.frame(beta.data) |
|
| 221 | 221 |
mod <- CAD~LDL+HDL+TG+TC |
| 222 | 222 |
pathvalue <- path(betav=mybeta, model=mod, outcome="CAD") |
| 223 | 223 |
|
| ... | ... |
@@ -260,7 +260,7 @@ disease="CAD", R2D=0.536535,R2O=0.988243) |
| 260 | 260 |
################################################### |
| 261 | 261 |
|
| 262 | 262 |
data(SNP358.data) |
| 263 |
-SNP358 <- DataFrame(SNP358.data) |
|
| 263 |
+SNP358 <- as.data.frame(SNP358.data) |
|
| 264 | 264 |
head(SNP358) |
| 265 | 265 |
|
| 266 | 266 |
################################################### |
| ... | ... |
@@ -284,7 +284,7 @@ snpPositAnnot(SNPdata=SNP358,SNP_hg19="chr",main="A") |
| 284 | 284 |
################################################### |
| 285 | 285 |
|
| 286 | 286 |
data(SNP368annot.data) |
| 287 |
-SNP368<-DataFrame(SNP368annot.data) |
|
| 287 |
+SNP368<-as.data.frame(SNP368annot.data) |
|
| 288 | 288 |
SNP368[1:10, ] |
| 289 | 289 |
|
| 290 | 290 |
################################################### |
| ... | ... |
@@ -11,7 +11,6 @@ |
| 11 | 11 |
<<style, echo=FALSE, results=tex>>= |
| 12 | 12 |
BiocStyle::latex(use.unsrturl=FALSE) |
| 13 | 13 |
@ |
| 14 |
- |
|
| 15 | 14 |
\title{GWAS-based Mendelian Randomization Path Analysis}
|
| 16 | 15 |
\author{Yuan-De Tan \\
|
| 17 | 16 |
\texttt{tanyuande@gmail.com}}
|
| ... | ... |
@@ -81,8 +80,8 @@ dim(data1) |
| 81 | 80 |
dim(data2) |
| 82 | 81 |
colnames(data1) <- c("SNP", "var1", "var2", "var3", "var4")
|
| 83 | 82 |
colnames(data2) <- c("SNP", "var1", "var2", "var3", "var4", "V1", "V2", "V3")
|
| 84 |
-data1<-DataFrame(data1) |
|
| 85 |
-data2<-DataFrame(data2) |
|
| 83 |
+data1<-as.data.frame(data1) |
|
| 84 |
+data2<-as.data.frame(data2) |
|
| 86 | 85 |
data12 <- fmerge(fl1=data1, fl2=data2, ID1="SNP", ID2="SNP", A=".dat1", B=".dat2", method="No") |
| 87 | 86 |
@ |
| 88 | 87 |
|
| ... | ... |
@@ -272,7 +271,7 @@ To roughly display relationship of the undefined causal variables to disease of |
| 272 | 271 |
|
| 273 | 272 |
<<>>= |
| 274 | 273 |
data(beta.data) |
| 275 |
-beta.data<-DataFrame(beta.data) |
|
| 274 |
+beta.data<-as.data.frame(beta.data) |
|
| 276 | 275 |
CAD <- beta.data$cad |
| 277 | 276 |
LDL <- beta.data$ldl |
| 278 | 277 |
HDL <- beta.data$hdl |
| ... | ... |
@@ -307,7 +306,7 @@ After \textbf{standard beta table} was successfully created by \Rfunction{mktabl
|
| 307 | 306 |
|
| 308 | 307 |
<<path, keep.source=TRUE, eval=FALSE>>= |
| 309 | 308 |
data(beta.data) |
| 310 |
-mybeta <- DataFrame(beta.data) |
|
| 309 |
+mybeta <- as.data.frame(beta.data) |
|
| 311 | 310 |
mod <- CAD~LDL+HDL+TG+TC |
| 312 | 311 |
pathvalue <- path(betav=mybeta, model=mod, outcome="CAD") |
| 313 | 312 |
@ |
