net2pred: Function fitting a regression model for each gene in the data

In predictionet: Inference for predictive networks designed for (but not limited to) genomic data

Description

Function to fit a regression model for each variable in the dataset or alternatively each variable of interest.

Usage

net2pred(net, data, categories, predn, perturbations, method = c("linear", "linear.penalized", "cpt"), seed)

Arguments

net	network object.
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 discretized; 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' and categories is missing, data should contain categorical values and the number of categories will determine from the data.
predn	indices or names of variables to fit during network inference. If missing, all the variables will be used for network inference.
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.
method	type of predictive model to fit; linear for linear regression model, linear.penalized for regularized linear regression model, cpt for conditional probability tables estimated after discretization of the data.
seed	set the seed to make the cross-validation and network inference deterministic.

Value

a new network object with the predictive models

Author(s)

Benjamin Haibe-Kains, Catharina Olsen

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)
## create matrix of perturbations (no perturbations in this dataset)
pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), dimnames=dimnames(data.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
mypert <- pert[ , goi, drop=FALSE]

########################
## regression-based network inference
########################
## infer global network from data and priors
mynet <- netinf(data=mydata, perturbations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", seed)

net2pred(net=mynet, data=mydata, method="linear")

predictionet documentation built on Nov. 8, 2020, 7:48 p.m.