kendallfdrci {GeneNT} | R Documentation |
This function implement the two-stage screening procedure based on Kendall correlation coefficient. Specifying a pair of FDR and MAS criteria, the algorithm provides an initial co-expression discovery that controls only FDR, which is then followed by a second stage co-expression discovery which controls both FDR and MAS.
kendallfdrci(Q, cormin)
Q |
The significant level |
cormin |
The specified minimum acceptable strength of association measured using Kendall correlation coefficient |
The data matrix file must be in the right format. The first row must be one shorter than the rest rows. The first column must be gene names.
The function returns a list of gene pairs that satisfies the FDR and MAS criteria simultaneously measured by Kendall correlation coefficient.
kG1 |
The gene pairs that passes Stage I (FDR only) screening |
kG2 |
The gene pairs that passed both Stage I (FDR) and II (MAS) screenings |
Dongxiao Zhu (http://www-personal.umich.edu/~zhud)
Zhu, D., Hero, A.O., Qin, Z.S. and Swaroop, A. High throughput screening of co-expressed gene pairs with controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS). Submitted.
Hollander M. and Wolfe D.A. (1999). Nonparametric Statistical Methods, New York: Wiley.
# load GeneNT and GeneTS library library(GeneTS) library(GeneNT) library(e1071) #EITHER use the example dataset data(dat) #OR use the following if you want to import external data #dat <- read.table("gal.txt", h = T, row.names = 1) #Note, data matrix name has to be "dat" #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs #g1 <- corfdrci(0.2, 0.5) #pG1 <- g1$pG1 #pG2 is the dataset containing gene pairs that passed two-stage screening #pG2 <- g1$pG2 #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs #g2 <- kendallfdrci(0.2, 0.5) #kG1 <- g2$kG1 #kG2 is the dataset containing gene pairs that passed two-stage screening #kG2 <- g2$kG2 #generate Pajek compatible matrix to visualize network #getBM(pG2, kG2) #clustering from network using network constraint clustering, for example, p = 3. #spclust(3, pG2, kG2)