choose_lambda {COSINE} | R Documentation |
Randomly sample a large number of subnetworks with the same size as the ones chosen by the five different lambda values to get the null distribution of the scores of subnetworks corresponding to different size and lambda, in order to get the adjusted scores for the chosen subnetworks, and choose the lambda giving rise to the highest scored sub-network
choose_lambda(diff_expr, diff_coex, lambda, subnet_size, num_random_sampling, best_score)
diff_expr |
A vector storing the F-statistics measuring the differential expression of each gene, which length equals the number of genes N |
diff_coex |
An N by N matrix with entry (i,j) corresponding to the ECF-statistics of gene pair (i,j), which measures the differential correlation between genes i and j |
lambda |
A numeric vector of length 5 which stores the five quantiles of weight parameter lambda |
subnet_size |
A numeric vector of length 5 which stores the size of subnetworks selected using different weight parameter lambda |
num_random_sampling |
the number of random subnetworks to be sampled for each lambda in order to get the null distribution |
best_score |
the best scores of the five sub-networks selected using genetic algorithm |
A list containing:
Adj_score |
The adjusted scores of the five selected sub-networks according to the null distribution generated by random sampling |
best_lambda |
The lambda giving rise to the sub-network with the highest adjusted score |
Random_Score |
The matrix storing the null score distribution of random subnetworks |
Haisu Ma
data(set1_scaled_diff) data(set1_GA) set1_quantile<-get_quantiles(diff_expr=set1_scaled_diff[[1]], diff_coex=set1_scaled_diff[[2]],klist=c(20,25),pop_size=5) lambda<-set1_quantile[[2]] set1_choose_lambda <- choose_lambda(diff_expr=set1_scaled_diff[[1]], diff_coex=set1_scaled_diff[[2]],lambda,subnet_size=set1_GA$Subnet_size, num_random_sampling=2,best_score=set1_GA$Best_Scores)