Description Usage Arguments Details Value Author(s) Examples
The algorithm to rank candidate regulators
1 2 3 4 |
DCGs |
a data frame or matrix for DCGs list. |
DCLs |
a data frame or matrix for DCLs list. |
tf2target |
a data frame or matrix for TF-to-target interaction pairs. |
expGenes |
a list for measured genes by array |
rank.method |
a character string indicating which ranking method to be utilized. The default is 'TED'. |
Nperm |
permutation times. If Nperm>0, the permutation step will be implemented for TED and TDD methods. The default value for Nperm is 0. |
DRrank is implemented for ranking potential TFs in terms of their relevance to the phenotypic change or biophysical process of interest. It contains two methods: TED, and TDD.
TED, short for 'Target Enrichment Density', employs Binomial Probability model to quantify the enrichment of a TF's targets in the DCG set, and as such to evaluate which regulators are more likely to be subject-relevant or even causal. Suppose we sift K DCGs from expression profile which contains N genes. If TFi has Ti targets in regulation knowledge, there should be Ti * K / N DCGs appeared in TFi targets list randomly. Actually, it is found that TI DCGs are included in TFi's targets list. The larger TI than Ti * K / N is, the more targets of TFi enriched, the more likely TFi is a relevant or causative regulator.
TDD, short for 'Targets' DCL Density', uses Clustering Coefficient to quantify the density of DCLs among a regulator's targets, and so to judge the importance of a TF. Suppose that TFi has n targets, and that there are k DCLs among these targets. A larger k means more DCLs are bridged by the common TFi. We intuitively assume that, if a TF bridged more TF_bridged_DCL it is of more importance (even if the regulator is not a DCG).
A matrix to display TED or TDD scores and ranks.
Jing Yang, Hui Yu
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | data(exprs)
## divide exprs into two parts corresponding to condition 1
## (exprs.1) and condition 2 (exprs.2) respectively
expGenes<-rownames(exprs)
exprs<-exprs[1:100,]
exprs.1<-exprs[1:100,1:16]
exprs.2<-exprs[1:100,17:63]
data(tf2target)
DCp.res<-DCp(exprs.1,exprs.2,
link.method = 'qth',cutoff=0.25)
DCe.res<-DCe(exprs.1,exprs.2,
link.method = 'qth',cutoff=0.25,nbins=10,p=0.1)
DCsum.res<-DCsum(DCp.res,DCe.res,DCpcutoff=0.25,DCecutoff=0.4)
## rank all the potential TFs
data(tf2target)
DRrank.TED.res<-DRrank(DCsum.res$DCGs, DCsum.res$DCLs,
tf2target, expGenes,
rank.method=c('TED','TDD')[1],
Nperm=0)
DRrank.TED.res[1:3,]
DRrank.TDD.res<-DRrank(DCsum.res$DCGs, DCsum.res$DCLs,
tf2target, expGenes,
rank.method=c('TED','TDD')[2],
Nperm=0)
DRrank.TDD.res[1:3,]
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