ncclust {GeneNT} | R Documentation |
This function does network constrained clustering based on Floyd-Warshall algorithm, R function allshortestpaths()in R packpage e1071.
ncclust(p, pG2, kG2)
p |
p is the exponential tuning factor with default value 1. it can also be set to other intergers whenever necessary. |
pG2 |
pG2 is the gene pairs that are screened using the two-stage algorithm based on Pearson correlation statistic. |
kG2 |
kG2 is the gene pairs that are screened using the two-stage algorithm based on Kendall correlation statistic. |
This function is written in comparison to the traditional clustering implemented as tdclust().
This function returns a network constrained distance matrix that can be used by any distance based clustering software.
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). J Comput Biol, in press. Zhu, D., Hero, A.O., Hong, C., Khanna, R., and Swaroop A. Network constrained clustering for gene microarray data. {it Submitted}
# load GeneNT and GeneTS library library(GeneTS) library(GeneNT) library(e1071) #EITHER use the internal 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$pG1 #pG2 contains gene pairs that passed two-stage screening #pG2 <- g1$pG2 #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs #g2 <- kendallfdrci(0.2, 0.5) #kG1 <- g2$kG1 #kG2 contains gene pairs that passed two-stage screening #kG2 <- g2$kG2 #generate Pajek compatible matrix to visualize network #getBM(pG2, kG2) #clustering from network using network constraint clustering #ncclust(3, pG2, kG2)