Mingsheng Zhang authored on 02/12/2018 21:01:39
| ... | ... |
@@ -37,27 +37,27 @@ network topology |
| 37 | 37 |
} |
| 38 | 38 |
\examples{
|
| 39 | 39 |
correlation.m<-matrix(0,12,12) |
| 40 |
-correlation.m[1,c(2:10)]=c(0.006,0.054,0.079,0.078, 0.011,0.033,0.014, |
|
| 40 |
+correlation.m[1,c(2:10)]<-c(0.006,0.054,0.079,0.078, 0.011,0.033,0.014, |
|
| 41 | 41 |
0.023,0.034) |
| 42 |
-correlation.m[2,c(3:10)]=c(0.026,0.014,0.045,0.037, 0.026,0.011,0.034, |
|
| 42 |
+correlation.m[2,c(3:10)]<-c(0.026,0.014,0.045,0.037, 0.026,0.011,0.034, |
|
| 43 | 43 |
0.012) |
| 44 |
-correlation.m[3,c(4:10)]=c(0.016,0.024,0.039,0.045, 0.009,0.003,0.028) |
|
| 45 |
-correlation.m[4,c(5:10)]=c(0.039,0.002,0.053,0.066, 0.012,0.039) |
|
| 46 |
-correlation.m[5,c(6:10)]=c(0.019,0.016,0.047,0.046, 0.013) |
|
| 47 |
-correlation.m[6,c(7:10)]=c(0.017,0.057,0.029,0.056) |
|
| 48 |
-correlation.m[7,c(8:10)]=c(0.071,0.018,0.001) |
|
| 49 |
-correlation.m[8,c(9:10)]=c(0.046,0.014) |
|
| 50 |
-correlation.m[9,10]=0.054 |
|
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-correlation.m[lower.tri(correlation.m)] = |
|
| 44 |
+correlation.m[3,c(4:10)]<-c(0.016,0.024,0.039,0.045, 0.009,0.003,0.028) |
|
| 45 |
+correlation.m[4,c(5:10)]<-c(0.039,0.002,0.053,0.066, 0.012,0.039) |
|
| 46 |
+correlation.m[5,c(6:10)]<-c(0.019,0.016,0.047,0.046, 0.013) |
|
| 47 |
+correlation.m[6,c(7:10)]<-c(0.017,0.057,0.029,0.056) |
|
| 48 |
+correlation.m[7,c(8:10)]<-c(0.071,0.018,0.001) |
|
| 49 |
+correlation.m[8,c(9:10)]<-c(0.046,0.014) |
|
| 50 |
+correlation.m[9,10]<-0.054 |
|
| 51 |
+correlation.m[lower.tri(correlation.m)] <- |
|
| 52 | 52 |
t(correlation.m)[lower.tri(correlation.m)] |
| 53 | 53 |
|
| 54 | 54 |
matrix.v<-matrix(0.5,5,12) |
| 55 | 55 |
matrix.v<-as.data.frame(matrix.v) |
| 56 |
-colnames(matrix.v)=c("NM_052960","NR_138250","NM_015074","NM_183416",
|
|
| 56 |
+colnames(matrix.v)<-c("NM_052960","NR_138250","NM_015074","NM_183416",
|
|
| 57 | 57 |
"NM_017891","NM_001330306","NM_014917","NM_001312688","NM_001330665", |
| 58 | 58 |
"NM_017766","NM_001079843","NM_001040709") |
| 59 | 59 |
modulecolor<-c(rep(c("yellow","cyan"),c(10,2)))
|
| 60 |
-module.topology=epihet::moduleVisual(correlation.m, |
|
| 60 |
+module.topology<-epihet::moduleVisual(correlation.m, |
|
| 61 | 61 |
value.matrix=matrix.v, |
| 62 | 62 |
moduleColors=modulecolor, |
| 63 | 63 |
mymodule="yellow",cutoff=0.02, |
Mingsheng Zhang authored on 21/11/2018 13:04:47
| 1 | 1 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,65 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/moduleVisual.R |
|
| 3 |
+\name{moduleVisual}
|
|
| 4 |
+\alias{moduleVisual}
|
|
| 5 |
+\title{Modules visualization and network topology}
|
|
| 6 |
+\usage{
|
|
| 7 |
+moduleVisual(TOM, value.matrix, moduleColors, mymodule, cutoff = 0.02, |
|
| 8 |
+ prefix = NULL, sve = FALSE) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{TOM}{the topological overlap matrix in WGCNA generated from
|
|
| 12 |
+the epiNetwork() function} |
|
| 13 |
+ |
|
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+\item{value.matrix}{A data frame generated from the epiNetwork() function.
|
|
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+the row name is patients in one subtype. the column name is the DEH loci |
|
| 16 |
+the value in the matrix is epigenetic heterogeneity on one DEH loci |
|
| 17 |
+for one patient} |
|
| 18 |
+ |
|
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+\item{moduleColors}{the module assignment generated from the epiNetwork()
|
|
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+function} |
|
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+ |
|
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+\item{mymodule}{a character vector containing the module colors
|
|
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+you want to visulaize} |
|
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+ |
|
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+\item{cutoff}{adjacency threshold for including edges in the output (default:0.02)}
|
|
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+ |
|
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+\item{prefix}{a character for output filename}
|
|
| 28 |
+ |
|
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+\item{sve}{A boolean to save the plot (default: FALSE)}
|
|
| 30 |
+} |
|
| 31 |
+\value{
|
|
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+a list containing all module edge and node information for mymodule |
|
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+} |
|
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+\description{
|
|
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+Visualize the modules identified by epiNetwork() function, and calculate |
|
| 36 |
+network topology |
|
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+} |
|
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+\examples{
|
|
| 39 |
+correlation.m<-matrix(0,12,12) |
|
| 40 |
+correlation.m[1,c(2:10)]=c(0.006,0.054,0.079,0.078, 0.011,0.033,0.014, |
|
| 41 |
+0.023,0.034) |
|
| 42 |
+correlation.m[2,c(3:10)]=c(0.026,0.014,0.045,0.037, 0.026,0.011,0.034, |
|
| 43 |
+0.012) |
|
| 44 |
+correlation.m[3,c(4:10)]=c(0.016,0.024,0.039,0.045, 0.009,0.003,0.028) |
|
| 45 |
+correlation.m[4,c(5:10)]=c(0.039,0.002,0.053,0.066, 0.012,0.039) |
|
| 46 |
+correlation.m[5,c(6:10)]=c(0.019,0.016,0.047,0.046, 0.013) |
|
| 47 |
+correlation.m[6,c(7:10)]=c(0.017,0.057,0.029,0.056) |
|
| 48 |
+correlation.m[7,c(8:10)]=c(0.071,0.018,0.001) |
|
| 49 |
+correlation.m[8,c(9:10)]=c(0.046,0.014) |
|
| 50 |
+correlation.m[9,10]=0.054 |
|
| 51 |
+correlation.m[lower.tri(correlation.m)] = |
|
| 52 |
+t(correlation.m)[lower.tri(correlation.m)] |
|
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+ |
|
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+matrix.v<-matrix(0.5,5,12) |
|
| 55 |
+matrix.v<-as.data.frame(matrix.v) |
|
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+colnames(matrix.v)=c("NM_052960","NR_138250","NM_015074","NM_183416",
|
|
| 57 |
+"NM_017891","NM_001330306","NM_014917","NM_001312688","NM_001330665", |
|
| 58 |
+"NM_017766","NM_001079843","NM_001040709") |
|
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+modulecolor<-c(rep(c("yellow","cyan"),c(10,2)))
|
|
| 60 |
+module.topology=epihet::moduleVisual(correlation.m, |
|
| 61 |
+ value.matrix=matrix.v, |
|
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+ moduleColors=modulecolor, |
|
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+ mymodule="yellow",cutoff=0.02, |
|
| 64 |
+ prefix='CEBPA_sil_epipoly',sve = TRUE) |
|
| 65 |
+} |