A.estimation.Srow {NTW} | R Documentation |
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 .
A.estimation.Srow(r, cMM.corrected, pred.net, X, P.known, topD, restK, cFlag, sup.drop, noiseLevel)
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. |
A.row |
A vector of estimated row r in A. |
Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang,Yuanhua Liu, Christine Nardini
##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