predictionet (version 1.18.0)

netinf.predict: Function to make prediction of a node values given its parents using an inferred network

Description

This function predict the value of a node given its parents using an inferred network

Usage

netinf.predict(net, data, categories, perturbations, subset, predn, method=c("linear", "linear.penalized", "cpt"))

Arguments

net

a network object with local regression models.

data

matrix of continuous or categorical values (gene expressions for example); observations in rows, features in columns.

categories

if this parameter missing, 'data' should be already discretize; otherwise either a single integer or a vector of integers specifying the number of categories used to discretize each variable (data are then discretized using equal-frequency bins) or a list of cutoffs to use to discretize each of the variables in 'data' matrix. If method='bayesnet', this parameter should be specified by the user.

perturbations

matrix of 0, 1 specifying whether a gene has been perturbed (e.g., knockdown, overexpression) in some experiments. Dimensions should be the same than data.

subset

vector of indices to select only subset of the observations.

predn

indices or names of variables to fit during network inference. If missing, all the variables will be used for network inference.

method

regrnet for regression-based network inference, bayesnet for bayesian network inference with the catnet package.

Value

matrix of predicted values

Examples

## load gene expression data for colon cancer data, list of genes related to RAS signaling pathway and the corresponding priors
data(expO.colon.ras)
## number of genes to select for the analysis
genen <- 10
## select only the top genes
goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fold.change"])), decreasing=TRUE)[1:genen]]
mydata <- data.ras[ , goi, drop=FALSE]
myannot <- annot.ras[goi, , drop=FALSE]
mypriors <- priors.ras[goi, goi, drop=FALSE]
mydemo <- demo.ras
## infer global network from data and priors
mynet <- netinf(data=mydata, priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", seed=54321)
mynet <- net2pred(net=mynet, data=mydata, method="linear")

## predict gene expression of the first gene
mypreds <- netinf.predict(net=mynet, data=mydata, predn=goi[1])[ ,goi[1]]
## root mean squared error (RMSE)
nrmse <- sqrt(mean((mydata[ ,goi[1]] - mypreds)^2))
## R2
r2 <- cor(mydata[ ,goi[1]], mypreds)^2
plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression", ylab="Predicted gene expression")