JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data
Cite: Ji, J., He, D., Feng, Y., He, Y., Xue, F., & Xie, L. (2017). JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data. Bioinformatics. doi: 10.1093/bioinformatics/btx360
R code avaiable from https://github.com/jijiadong/JDINAC/blob/master/jdinac.r
jdinac(EDGE, classLabel, DataFit, DataPre, zFit=NULL, zPre=NULL, nsplit=10, nfolds=5)
- DataFit, DataPre: data matrices containing one sample per row, one variable per column. DataFit: training data, DataPre: prediction data.
- zFit, zPre: covariate in training data and prediction data respectively.
- classLabel: must be 0 or 1, e.g. 1 for cases and 0 for controls.
- nsplit: randomly split the dataset 2*nsplit times.
- nfolds: number of folds for Cross-validation.
- EDGE: array indices. which edge will be tested.
- yPre: predicted value of response.
- Eset: differential edge set; the first two columns are the array indices; the edge between row-th variable and col-th variable. The 3rd column (numb) is the differential dependency weight; not normalized.
Run the R code jdinac.r first.
library(mvtnorm)
Sigma0 <- 0.5* (-1)^abs(outer(1:10,1:10,"-"))
Sigma1 <- array(0.5,dim=c(10,10))
adjM <- array(1,dim=c(10,10))
adjM[lower.tri(adjM,diag=T)] <- 0
adjM[1,5] <- adjM[3,6] <- adjM[4,8] <- 0
adjM
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0 1 1 1 0 1 1 1 1 1
## [2,] 0 0 1 1 1 1 1 1 1 1
## [3,] 0 0 0 1 1 0 1 1 1 1
## [4,] 0 0 0 0 1 1 1 0 1 1
## [5,] 0 0 0 0 0 1 1 1 1 1
## [6,] 0 0 0 0 0 0 1 1 1 1
## [7,] 0 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 0 0 1 1
## [9,] 0 0 0 0 0 0 0 0 0 1
## [10,] 0 0 0 0 0 0 0 0 0 0
EDGE <- which(adjM!=0, arr.ind=T)
head(EDGE)
## row col
## [1,] 1 2
## [2,] 1 3
## [3,] 2 3
## [4,] 1 4
## [5,] 2 4
## [6,] 3 4
set.seed(2017)
size0 <- size1 <- 50
class0 <- rmvnorm( n = size0, sigma = Sigma0,method = "svd")
class1 <- rmvnorm( n = size1, sigma = Sigma1,method = "svd" )
dataset <- rbind(class0,class1)
classLabel <- c(rep(0,size0),rep(1,size1))
difnet <- jdinac(EDGE=EDGE,classLabel=classLabel,DataFit=dataset,DataPre=dataset,nsplit=10,nfolds=5)
head(difnet$yPre)
## [1] 3.893804e-01 3.459244e-08 9.624137e-07 8.148533e-09 4.774174e-07
## [6] 3.384799e-05
head(difnet$Eset)
## row col numb
## 1 1 2 20
## 3 2 3 16
## 5 2 4 14
## 6 3 4 13
## 11 2 6 13
## 21 2 8 13
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