NTW: Estimation of gene interaction matrix A and perturbation...

In NTW: Predict gene network using an Ordinary Differential Equation (ODE) based method

Description

This function is used to estimate the whole gene interaction matrix A and the perturbation targets matrix P, row-wise, using the NTW algorithm (see reference), based on ODE method. In this method, the linearized ODE can solved using 3 regression methods: geo, sse and ml. In order to save computation time, and improve results, NTW offers the opportunity to input gene association information output from other algorithms or from the literature. The non-null regressors in the gene association network will help fix the regressors to be estimated in the final matrix A. Two ways are supplied to use the non-zero information, namely forward and backward approaches. In the "backward" pattern, only the non-zero positions in the prior gene association network will be used as regressors in A. While in the "forward" pattern, both these non-zero positions and some other possible positions (depending on restK ) in A are used as regressors.

Usage

NTW(X, restK, topD, topK = NULL, P.known = NULL, cFlag, pred.net = NULL, sup.drop = -1, numP = NULL, noiseLevel = 0.1)

Arguments

X	Gene expression data, a matrix with genes as rows and perturbations as columns.
restK	A vector (length equals to nrow(A)) with each element to indicate the number of non-zero regressors in the corresponding row of A.
topD	A parameter in NTW algorithm for keeping the top topD combinations of non-zero regressors of a single row in A, see vignette for details.
topK	The number of possible targets of the perturbations, used for pre-estimate the perturbation targets matrix P .
P.known	A known P matrix with the same dimensions of X.
cFlag	A flag to tell the regression methods, "geo" for geometric mean method, "sse" for sum of square method and "ml" for maximum likelihood method.
pred.net	A matrix with the same dimensions of A for the prior gene association information. Default is NULL.
sup.drop	An indication to show the pattern for using the prior gene association information. 1 for "forward" pattern and -1 for "backward" pattern.
numP	A number set to limit the possibilities that one gene will be targeted by perturbations. That is at most numP perturbations can directly perturb one gene.
noiseLevel	Only used in ml method, to indicate the noise level in each perturbed experiment.

Value

est.A	Estimated gene interaction matrix A, with genes as rows and columns.
est.P	Estimated perturbation targets matrix P, with genes as rows and perturbations as columns.

Author(s)

Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang,Yuanhua Liu, Christine Nardini

References

Applied method for the inference of gene networks: the bifidobacterium case. to be submitted

Examples

##NTW testing without prior gene association information, regression is done by "sse"##
data(sos.data)
X<-sos.data
X<-as.matrix(X)
restK=rep(ncol(X)-1, nrow(X))
topD = round(0.6*nrow(X))
topK = round(0.5*nrow(X))
numP = round(0.25*nrow(X))
result<-NTW(X, restK, topD, topK, P.known=NULL, cFlag="sse", 
              pred.net = NULL, sup.drop = -1,numP, noiseLevel=0.1)
result$est.A
result$est.P

##NTW testing with prior gene association information, regression is done by "geo"##
pred.net<-matrix(round(runif(nrow(X)*nrow(X), min=0, max=1)), nrow(X), nrow(X))
result<-NTW(X, restK, topD, topK, P.known=NULL, cFlag="geo",
              pred.net, sup.drop = -1,numP, noiseLevel=0.1)
result$est.A
result$est.P

NTW documentation built on Nov. 8, 2020, 6:51 p.m.