R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for a HTML browser interface to help.
Type 'q()' to quit R.

> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("GeneNT-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('GeneNT')
Loading required package: e1071
Loading required package: class
Loading required package: GeneTS
Loading required package: locfdr
Loading required package: graph
Loading required package: cluster
Loading required package: Ruuid
Creating a new generic function for 'print' in 'Ruuid'
Loading required package: RBGL

Attaching package: 'RBGL'


	The following object(s) are masked from package:e1071 :

	 extractPath 

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "BEST.kendall"
> 
> ### * BEST.kendall
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BEST.kendall
> ### Title: BLAST type search for similar co-expressions with controlled FDR
> ###   and MAS using Kendall correlation statistic
> ### Aliases: BEST.kendall
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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) and seed gene "GAL7" as example to screen gene pairs 
> #g5 <- BEST.kendall("GAL7", 0.2, 0.5)  
> #bkG1 <- g5$bkG1
> 
> #bkG2 contains gene pairs that passed the two-stage screening
> #bkG2 <- g5$bkG2  
> 
> 
> 
> cleanEx(); ..nameEx <- "BEST.pearson"
> 
> ### * BEST.pearson
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BEST.pearson
> ### Title: BLAST type search for similar co-expressions with controlled FDR
> ###   and MAS using Pearson correlation statistic
> ### Aliases: BEST.pearson
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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) and seed gene "GAL7" as example to screen gene pairs 
> #g4 <- BEST.pearson("GAL7", 0.2, 0.5)  
> #bpG1 <- g4$bpG1
> 
> #bpG2 contains gene pairs that passed two-stage screening
> #bpG2 <- g4$bpG2  
> 
> 
> 
> cleanEx(); ..nameEx <- "cor.confint"
> 
> ### * cor.confint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cor.confint
> ### Title: Asymptotic confidence intervals of Pearson correlation
> ###   coefficient
> ### Aliases: cor.confint
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> #simulate a vector of correlation coefficients 
> #cor <- runif(20, -1, 1) 
> #g6 <- cor.confint(cor, 20, 0.05)
> #g6$conf.int1
> #g6$conf.int2
> 
> 
> 
> cleanEx(); ..nameEx <- "cor.rep"
> 
> ### * cor.rep
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cor.rep
> ### Title: Estimating gene expression correlation from replicated
> ###   microarray data
> ### Aliases: cor.rep
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneNT)
> d0 <- rnorm(100)
> for(l in 2:10)
+ d0 <- rbind(d0, rnorm(100))
> d0<- t(d0)
> M <- cor.rep(d0, m = 4, G= 25)
> 
> 
> 
> cleanEx(); ..nameEx <- "cor.rep.bootci"
> 
> ### * cor.rep.bootci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cor.rep.bootci
> ### Title: Bootstrap Confidence Interval for multivariate correlation
> ###   estimator from replicated gene microarray data
> ### Aliases: cor.rep.bootci
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneNT)
> d0 <- rnorm(8)
> #parameters are set to small values for quick demo purposes
> for(l in 2:3)
+ d0 <- rbind(d0, rnorm(8))
> d0<- t(d0)
> M <- cor.rep(d0, m = 4, G= 2)
> M.bootci <- cor.rep.bootci(d0, m = 4, G= 2, alpha = 0.05)
> 
> 
> 
> cleanEx(); ..nameEx <- "cor.rep.pv"
> 
> ### * cor.rep.pv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cor.rep.pv
> ### Title: Multivariate correlation estimator from replicated gene
> ###   microarray data
> ### Aliases: cor.rep.pv
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneNT)
> d0 <- rnorm(100)
> #sample size is set to 3 for quick demo purposes
> for(l in 2:3)
+ d0 <- rbind(d0, rnorm(100))
> d0<- t(d0)
> M <- cor.rep(d0, m = 4, G= 25)
> M.pv <- cor.rep.pv(d0, m = 4, G= 25)
> 
> 
> 
> cleanEx(); ..nameEx <- "corfdrci"
> 
> ### * corfdrci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corfdrci
> ### Title: Two-stage screening procedure for screening gene pairs based on
> ###   Pearson correlation statistic
> ### Aliases: corfdrci
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> 
> # 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$pG1
> #pG2 contains gene pairs that passed two-stage screening
> #pG2 <- g1$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, for example, p = 3.
> #spclust(3, pG2, kG2) 
> 
> 
> 
> cleanEx(); ..nameEx <- "corfdrci.inv"
> 
> ### * corfdrci.inv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corfdrci.inv
> ### Title: Inverse screening procedure based on Pearson correlation
> ###   coefficient
> ### Aliases: corfdrci.inv
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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"
> #Subset data to run faster
> dat <- dat[1:10,]
> #Set MAS level cormin = 0.8, calculate fdr adjusted p-values. 
> #fdrp8 <- corfdrci.inv(0.8)                                   
> 
> 
> 
> cleanEx(); ..nameEx <- "dat"
> 
> ### * dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dat
> ### Title: Part of galactose metabolism data. Ideker et al. Science,
> ###   292:929-934
> ### Aliases: dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(dat)
> boxplot(dat, xlab = "different experimental conditions", ylab = "log2 ratio of cy3/cy5 intensities") 
> 
> 
> 
> cleanEx(); ..nameEx <- "getBM"
> 
> ### * getBM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: getBM
> ### Title: Generate Pajek compatible matrix from screened gene pairs
> ### Aliases: getBM
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> 
> # 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$pG1
> #pG2 contains gene pairs that passed two-stage screening
> #pG2 <- g1$pG2  
> #g2 <- kendallfdrci(0.2, 0.5) #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs 
> #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, for example. p=3.
> #spclust(3, pG2, kG2) 
> 
> 
> 
> cleanEx(); ..nameEx <- "kendall.confint"
> 
> ### * kendall.confint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kendall.confint
> ### Title: Asymptotic confidence intervals of Pearson correlation
> ###   coefficient
> ### Aliases: kendall.confint
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> x <- runif(20, -1, 1)
> y <- runif(20, -1, 1)
> #kendall.confint(x,y,0.05)
> 
> 
> 
> cleanEx(); ..nameEx <- "kendallfdrci"
> 
> ### * kendallfdrci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kendallfdrci
> ### Title: Two-stage screening procedure based on Kendall correlation
> ###   coefficient
> ### Aliases: kendallfdrci
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # 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) 
> 
> 
> 
> cleanEx(); ..nameEx <- "ncclust"
> 
> ### * ncclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ncclust
> ### Title: Network constrained clustering
> ### Aliases: ncclust
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # 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) 
> 
> 
> 
> cleanEx(); ..nameEx <- "pcor.confint"
> 
> ### * pcor.confint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcor.confint
> ### Title: Asymptotic confidence intervals of partial correlation
> ###   coefficient
> ### Aliases: pcor.confint
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> pcor <- runif(20, -1, 1) #simulate a vector of correlation coefficients 
> #g7 <- pcor.confint(pcor, 20, 0.05)
> #g7$conf.int1
> #g7$conf.int2
> 
> 
> 
> cleanEx(); ..nameEx <- "pcorfdrci"
> 
> ### * pcorfdrci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcorfdrci
> ### Title: Two-stage screening procedure based on partial correlation
> ###   coefficient in Graphic Gaussian Model framework
> ### Aliases: pcorfdrci
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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, 0.2) as example to screen gene pairs 
> #g3 <- pcorfdrci(0.2, 0.2) 
> #G1 <- g3$G1.all
> #G2 is the dataset containing gene pairs that passed two-stage screening
> #G2 <- g3$G2  
> 
> 
> 
> cleanEx(); ..nameEx <- "pcorfdrci.inv"
> 
> ### * pcorfdrci.inv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcorfdrci.inv
> ### Title: Inverse screening procedure based on Kendall correlation
> ###   coefficient
> ### Aliases: pcorfdrci.inv
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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"
> #Subset data to run faster
> dat <- dat[1:10,]
> #Set MAS level pcormin = 0.8, calculate fdr adjusted p-values. 
> #fdrp8 <- pcorfdrci.inv(0.8)                                   
> 
> 
> 
> cleanEx(); ..nameEx <- "sm.name"
> 
> ### * sm.name
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sm.name
> ### Title: Extract gene names from data matrix
> ### Aliases: sm.name
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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"
> #calculate correlation matrix
> M <- cor(t(dat)) 
> #extract gene names (matrix object) from the correlation matrix
> #NL <- sm.name(M) 
> 
> 
> 
> cleanEx(); ..nameEx <- "tdclust"
> 
> ### * tdclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tdclust
> ### Title: Traditional clustering
> ### Aliases: tdclust
> ### Keywords: cluster htest
> 
> ### ** Examples
> 
> # load GeneNT and GeneTS library
> library(GeneTS)
> library(GeneNT)
> 
> #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"
> #Regular hierarchical clustering, for example, p = 3.
> #tdclust(3)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  8.72 0.22 8.98 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')