A.estimation.Srow: Estimation of a single row in matrix A with the perturbation...

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

Description

Estimating a single row of gene interaction matrix A when the perturbation targets matrix P is given. The single row in A is then regressed according to the equation AX=P with one of the three regression methods, geo, sse and ml .

Usage

A.estimation.Srow(r, cMM.corrected, pred.net, X, P.known, topD, restK, cFlag, sup.drop, noiseLevel)

Arguments

r	A number indicating the row of A to be estimated.
cMM.corrected	A flag to indicate whether a prior network is applied.
pred.net	A matrix with the same dimensions of A for prior network, which should be specified if cMM.corrected is 1, default is NULL.
X	Gene expression data, a matrix with genes as rows and perturbations as columns.
P.known	A known P matrix with the same dimensions of X.
topD	A parameter in NTW algorithm for keeping the top topD combinations of non-zero regressors of row r in A, see vignette for details.
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.
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.
sup.drop	An indication to identify the pattern for using the prior gene association information. 1 for "forward" pattern and -1 for "backward" pattern, see vignette for details.
noiseLevel	Only used in "ml" method, to indicate the noise level in each perturbed experiment.

Value

A.row

A vector of estimated row r in A.

Author(s)

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

Examples

##single row estimation without prior gene association information, regression is done by "sse"##
data(sos.data)
X<-sos.data
X<-as.matrix(X)
P.known<-matrix(round(runif(nrow(X)*ncol(X), min=0, max=1)), nrow(X), ncol(X))
restK=rep(ncol(X)-1, nrow(X))
topD = round(0.6*nrow(X))
topK = round(0.5*nrow(X))
result<-A.estimation.Srow(r=1,cMM.corrected = 0, pred.net= NULL,X,P.known, topD, restK, 
              cFlag="sse",sup.drop = -1, noiseLevel=0.1)
result$A.row

##single row estimation 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), ncol(X))
result<-A.estimation.Srow(r=1,cMM.corrected = 1, pred.net,X,P.known,topD, restK,
             cFlag="geo",sup.drop = -1, noiseLevel=0.1)
result$A.row

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