The function estimates the null distribution of coexpression patterns and generates coexpression network
create_network(so, alpha = 0.05, manual = FALSE, least_edge_prop = 0.01)SiFINeT object with est_ms (estimated mean and sd) and thres (network edge threshold) updated.
a SiFINeT object
the Type I error rate used for FDR control procedure
whether to manually set threshold for edge assignment
the minimum proportion of edges. Only used when manual = TRUE
Theoretically the distribution of coexpression patterns would converge to standard Gaussian if either one of the gene pair is not feature gene. However in genomics analysis, empirical null could be much more variable than theoretical null. SiFINeT uses estimated null mean and standard deviation to find the threshold for network edges. An edge is assigned to a pair of gene if the absolute value of coexpression pattern between the 2 genes is greater than the threshold Assuming the distribution to be Gaussian, with the estimated null mean and standard deviation, SiFINeT uses SQUAC to control the false discovery rate (FDR) for coexpression patterns. In case the signal is not strong enough and the coexpression network is too sparse, SiFINeT also accept user-defined lower bound for the least proportion of edges. Usually a coexpression network with edge proportion between 0.5% - 10% would have better performance for the detection of feature gene sets.
Jiashun Jin and Tony T. Cai. “Estimating the Null and the Proportion of Non-Null Effects in Large-Scale Multiple Comparisons”. In: Journal of the American Statistical Association 102 (478 2004), pp. 495–506. doi: 10.1198/016214507000000167.
Jichun Xie and Ruosha Li. “False discovery rate control for high dimensional networks of quantile associations conditioning on covariates”. In: J R Stat Soc Series B Stat Methodol (2018). doi: 10.1111/rssb.12288.