To quantify the similarity of the gene transcript abundance profiles, Pearson's correlation coefficients (PCC) of each gene pair, was calculated following the formulas of the online help page (http://atted.jp/help/coex_cal.shtml) and further transformed into Mutual Rank (MR) value with the method descripted (http://atted.jp/help/mr.shtml).shtml
There are several kinds of methods to evaluate the strength of co-expression, such as Pearson correlation coefficient (PCC), mutual rank (MR) based on rank transformations of the weighted PCC (Obayashi and Kinoshita[2009]) and correspondence analysis (CA) (Yano et al.[2006]).[2006]
The co-expression networks are drawn based on mutual rank information for the seven candidate genes.
Moreover, the mutual rank for coexpression of these two genes is the highest in their respective gene networks.
Mutual Rank (MR) values were used to evaluate the correlation between gene A and gene B in ATTED-II.
To estimate the effect of chromatin organization, we test the correlation between Hi-C interaction (observed Hi-C interaction numbers, OH, and Pearson correlation coeffecient of them, PC, of 1 M and 100 k resolution of both human gm06990 and K562 cells, from Ref. [ 10], see Methods for details) and mutual ranks of gene co-expression rates (provided by COXPRESdb[ 11]).
We extended the CLR method through integrating several gene-gene association estimation methods, of which includes Pearson, Spearman correlation [ 6], Kendall, Theil-sen [ 23], and Weighted Rank methods [ 24] as well as the mutual information-based method proposed in the original CLR method [ 9], in the DeGNServer.
In this model, the length of the list does not matter but rather the mutual ranking.
The edges were weighted by mutual ranks (MRs) of co-expressions between any two genes [ 6].
To evaluate the strength of co-expression a mutual ranking (MR) value was calculated using the formula MR AB) = (rank(A→B)* rank(B→A))0.5.
