## ----message=FALSE------------------------------------------------------------ library(KBoost) data(D4_multi_1) grn = kboost(D4_multi_1) grn$GRN[91:93,2:5] ## ----message=FALSE------------------------------------------------------------ library(KBoost) data(D4_multi_1) # Matrix of size 100x100 with all values set to 0.5 prior_weights = matrix(0.5,100,100) # For this example assume we know from previous experiments that TF2 regulates the gene in row 91 prior_weights[91,2] = 0.8 grn = kboost(X=D4_multi_1, prior_weights=prior_weights) # Note that the first entry now has a slightly higher probability than in the # previous example, as a result of adding the prior grn$GRN[91:93,2:5] ## ----message=FALSE------------------------------------------------------------ library(KBoost) # A random 10x5 numerical matrix X = rnorm(50,0,1) X = matrix(X,10,5) # Gene names corresponding to the columns of X gen_names = c("TP53","YY1","CTCF","MDM2","ESR1") grn = KBoost_human_symbol(X,gen_names,pos_weight=0.6, neg_weight=0.4) # TFs are taken from Lambert et al., 4 columns in the output network indicates 4 of the genes are TFs. grn$GRN # Look at the prior weights based on the Gerstein network. # Output indicates the YY1-TP53 edge is present in the Gerstein network. grn$prior_weights ## ----message=FALSE------------------------------------------------------------ library(KBoost) data(D4_multi_1) Net = kboost(D4_multi_1) dist = net_dist_bin(Net$GRN,Net$TFs,0.1) ## ----message=FALSE------------------------------------------------------------ library(KBoost) data(D4_multi_1) Net = kboost(D4_multi_1) Net_Summary = net_summary_bin(Net$GRN) ## ----message=FALSE------------------------------------------------------------ data(D4_multi_1) ## ----message=FALSE------------------------------------------------------------ data(G_D4_multi_1) ## ----message=FALSE------------------------------------------------------------ data(Gerstein_Prior_ENET_2) ## ----message=FALSE------------------------------------------------------------ data(Human_TFs)