netEstByMethod: Network estimation function

In yunzhang813/yunr: Simulation study on deconvolution for network estimate

Description

This function takes simulated data and estimates network based on WGCNA (correlation-based), ARACNE (mutual information-based) or Bayesian algorithms.

Usage

netEstByMethod(data.list, method, softPower=1)

Arguments

data.list	a list of simulated data, e.g. list(data.pure, data.mixed, data.deconvoluted). The output of this function will be a list of estimated networks of the same order.
method	method used to estimate network. Options: WGCNA is correlation-based, ARACNE is mutual information-base, Bayesian is Bayesian network, and GLASSO.
softPower	soft power used in WGCNA. Default: softPower=1.

Details

This function uses adjacency() for method="WGCNA", minet() for method="ARACNE", hc(),bn.fit() for method="Bayesian", and simply var() for method="GLASSO" for network estimation.

Value

This function returns a list of estimated networks, coresponding to the input data list.

Examples

## Not run: 
## simulated data
data.list <- list(out.oneStepSim$expr.pure.T, out.oneStepSim$expr.mixed, out.deconv$expr.deconv)
names(data.list) <- c("pure", "mixed", "deconvoluted")

## one step analysis
net.WGCNA <- netEstByMethod(data.list, method="WGCNA")
net.ARACNE <- netEstByMethod(data.list, method="ARACNE")
net.Bayesian <- netEstByMethod(data.list, method="Bayesian")
net.GLASSO <- netEstByMethod(data.list, method="GLASSO")

## End(Not run)

yunzhang813/yunr documentation built on May 4, 2019, 7:45 p.m.