rankMyGenes: A function to rank the genes by the number of DC pairs in...
In EBcoexpress: EBcoexpress for Differential Co-Expression Analysis

Description Usage Arguments Value Author(s) References See Also Examples

This function uses a threshold to determine the names of the DC pairs. It then splits those pairs into their constituent genes and tables them. A sorted version of that table is then returned. This information may be useful for those investigating ‘differential hubbing’ – see the Hudson et al. reference for more information

1	rankMyGenes(emOut, thresh = 0.95, sep = "~")

`emOut`	The output of an ebCoexpressSeries function call
`thresh`	A threshold for determining whether a pair is DC. This may be set as a hard threshold (default is hard 5 threshold, as returned by crit.fun
`sep`	The separator used in the pair names

A sorted, named array of gene counts

John A. Dawson <jadawson@wisc.edu>

Dawson JA and Kendziorski C. An empirical Bayesian approach for identifying differential co-expression in high-throughput experiments. (2011) Biometrics. E-publication before print: http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01688.x/abstract

Hudson NJ, Reverter A, Dalrymple BP (2009) A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation. PLoS Comput Biol 5(5): e1000382. doi:10.1371/journal.pcbi.1000382

ebCoexpressSeries, crit.fun

data(fiftyGenes)
tinyCond <- c(rep(1,100),rep(2,25))
tinyPat <- ebPatterns(c("1,1","1,2"))
D <- makeMyD(fiftyGenes, tinyCond, useBWMC=TRUE)
set.seed(3)
initHP <- initializeHP(D, tinyCond)

zout <- ebCoexpressZeroStep(D, tinyCond, tinyPat, initHP)
rankMyGenes(zout)