Package: doppelgangR

Version: 1.6.0

Command: /home/biocbuild/bbs-3.6-bioc/R/bin/R CMD check --no-vignettes --timings doppelgangR_1.6.0.tar.gz

StartedAt: 2018-04-11 23:01:47 -0400 (Wed, 11 Apr 2018)

EndedAt: 2018-04-11 23:05:02 -0400 (Wed, 11 Apr 2018)

EllapsedTime: 194.8 seconds

RetCode: 0

Status:  OK

CheckDir: doppelgangR.Rcheck

Warnings: 0

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### Running command:
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###   /home/biocbuild/bbs-3.6-bioc/R/bin/R CMD check --no-vignettes --timings doppelgangR_1.6.0.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.6-bioc/meat/doppelgangR.Rcheck’
* using R version 3.4.4 (2018-03-15)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘doppelgangR/DESCRIPTION’ ... OK
* this is package ‘doppelgangR’ version ‘1.6.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘doppelgangR’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU or elapsed time > 5s
               user system elapsed
plot-methods 13.024  0.348  31.235
corFinder     5.424  0.292   6.423
doppelgangR   3.988  0.168   5.353
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘maintest.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: OK

* installing *source* package ‘doppelgangR’ ...
** R
** inst
** preparing package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (doppelgangR)

R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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 an HTML browser interface to help.
Type 'q()' to quit R.

> library(RUnit)
> library(Biobase)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> library(doppelgangR)
Loading required package: BiocParallel
> 
> ncor <- 0; npheno <- 0; nsmoking <- 0
> options(stringsAsFactors=FALSE)
> set.seed(1)
> m1 <- matrix(rnorm(1100), ncol=11)
> colnames(m1) <- paste("m", 1:11, sep="")
> rownames(m1) <- make.names(1:nrow(m1))
> n1 <- matrix(rnorm(1000), ncol=10)
> colnames(n1) <- paste("n", 1:10, sep="")
> rownames(n1) <- make.names(1:nrow(n1))
> ##m:1 & n:1 are expression doppelgangers:
> m1[, 1] <- n1[, 1] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##m:2 & m:3 are expression doppelgangers:
> m1[, 2] <- m1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:2 & n:3 are expression doppelgangers:
> n1[, 2] <- n1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:8 & n:9 are expression doppelgangers:
> n1[, 8] <- n1[, 9] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:4 & m:6 are expression doppelgangers:
> n1[, 4] <- m1[, 6] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:5 & m:4 are expression doppelgangers:
> n1[, 5] <- m1[, 4] + rnorm(100, sd=0.25); ncor <- ncor+1
> 
> ##
> ##m:10 and n:10 are phenotype doppelgangers:
> m.pdata <- matrix(letters[sample(1:26, size=110, replace=TRUE)], ncol=10)
> rownames(m.pdata) <- colnames(m1)
> n.pdata <- matrix(letters[sample(1:26, size=100, replace=TRUE)], ncol=10)
> n.pdata[10, ] <- m.pdata[10, ]; npheno <- npheno+1
> rownames(n.pdata) <- colnames(n1)
> 
> 
> 
> ##Create ExpressionSets
> m.eset <- ExpressionSet(assayData=m1, phenoData=AnnotatedDataFrame(data.frame(m.pdata)))
> m.eset$id <- toupper(colnames(m1))
> ##
> n.eset <- ExpressionSet(assayData=n1, phenoData=AnnotatedDataFrame(data.frame(n.pdata)))
> n.eset$id <- toupper(colnames(n1))
> ##m5 and n4 are "smoking gun" doppelgangers:
> n.eset$id[4] <- "gotcha"
> m.eset$id[5] <- "gotcha"
> nsmoking <- nsmoking+1
> ##
> esets <- list(m=m.eset, n=n.eset)
> 
> ##------------------------------------------
> ##Check of all three types of doppelgangers:
> ##------------------------------------------
> res1 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df1 <- summary(res1)
> 
> checkIdentical(df1[df1$sample1=="m:m1" & df1$sample2=="n:n1", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:m2" & df1$sample2=="m:m3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:m6" & df1$sample2=="n:n4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="n:n2" & df1$sample2=="n:n3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:m6" & df1$sample2=="n:n4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:m10" & df1$sample2=="n:n10", "pheno.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:m5" & df1$sample2=="n:n4", "smokinggun.doppel"], TRUE)
[1] TRUE
> checkEquals(nrow(df1), ncor+npheno+nsmoking)
[1] TRUE
> checkEquals(sum(df1$expr.doppel), ncor)
[1] TRUE
> checkEquals(sum(df1$pheno.doppel), npheno)
[1] TRUE
> checkEquals(sum(df1$smokinggun.doppel), nsmoking)
[1] TRUE
> checkEquals(sum(is.na(df1$expr.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$pheno.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$smokinggun.similarity)), 0)
[1] TRUE
> checkEquals(df1$id, c("M2:M3", "N2:N3", "N8:N9", "M1:N1", "gotcha:gotcha", "M6:gotcha", "M4:N5", "M10:N10"))
[1] TRUE
> for (i in match(paste("X", 1:10, sep=""), colnames(df1))){
+     cat(paste("Checking column", i, "\n"))
+     checkEquals(all(grepl("[a-z]:[a-z]", df1[[i]])), TRUE)
+ }
Checking column 9 
Checking column 10 
Checking column 11 
Checking column 12 
Checking column 13 
Checking column 14 
Checking column 15 
Checking column 16 
Checking column 17 
Checking column 18 
> 
> ##------------------------------------------
> cat("\n")

> cat("Check without smoking guns: \n")
Check without smoking guns: 
> ##------------------------------------------
> res2 <- doppelgangR(esets, smokingGunFinder.args=NULL, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df2 <- summary(res2)
> for (i in grep("pheno.similarity|smokinggun.similarity", colnames(df1), invert=TRUE)){
+     cat(paste("Checking column", i, "\n"))
+     checkEquals(df2[, i], df1[!df1$smokinggun.doppel, i])
+ }
Checking column 1 
Checking column 2 
Checking column 3 
Checking column 4 
Checking column 6 
Checking column 8 
Checking column 9 
Checking column 10 
Checking column 11 
Checking column 12 
Checking column 13 
Checking column 14 
Checking column 15 
Checking column 16 
Checking column 17 
Checking column 18 
Checking column 19 
> 
> 
> ##------------------------------------------
> cat("\n")

> cat("Check without phenotype: \n")
Check without phenotype: 
> ##------------------------------------------
> res3 <- doppelgangR(esets, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Finalizing...
> df3 <- summary(res3)
> for (i in grep("pheno.similarity", colnames(df1), invert=TRUE)){
+     cat(paste("Checking column", i, "\n"))
+     checkEquals(df3[, i], df1[!df1$pheno.doppel, i])
+ }
Checking column 1 
Checking column 2 
Checking column 3 
Checking column 4 
Checking column 6 
Checking column 7 
Checking column 8 
Checking column 9 
Checking column 10 
Checking column 11 
Checking column 12 
Checking column 13 
Checking column 14 
Checking column 15 
Checking column 16 
Checking column 17 
Checking column 18 
Checking column 19 
> 
> ##------------------------------------------
> cat("\n")

> cat("Check without expression: \n")
Check without expression: 
> ##------------------------------------------
> res4 <- doppelgangR(esets, corFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df4 <- summary(res4)
> for (i in grep("expr.similarity", colnames(df1), invert=TRUE)){
+     cat(paste("Checking column", i, "\n"))
+     checkEquals(df4[, i], df1[!df1$expr.doppel, i])
+ }
Checking column 1 
Checking column 2 
Checking column 4 
Checking column 5 
Checking column 6 
Checking column 7 
Checking column 8 
Checking column 9 
Checking column 10 
Checking column 11 
Checking column 12 
Checking column 13 
Checking column 14 
Checking column 15 
Checking column 16 
Checking column 17 
Checking column 18 
Checking column 19 
> 
> ##------------------------------------------
> cat("\n")

> cat("Check smoking guns only: \n")
Check smoking guns only: 
> ##------------------------------------------
> res4b <- doppelgangR(esets, corFinder.args=NULL, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Finalizing...
> df4b <- summary(res4b); rownames(df4b) <- NULL
> df4b.compare <- df1[df1$smokinggun.doppel, ]; rownames(df4b.compare) <- NULL
> checkIdentical(df4b.compare[, -3:-6], df4b[, -3:-6])  ##don't check expr and pheno columns
[1] TRUE
> 
> 
> ##------------------------------------------
> cat("\n")

> cat("Check pruning: \n")
Check pruning: 
> ##------------------------------------------
> res5 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, intermediate.pruning=TRUE, cache.dir=NULL)
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df5 <- summary(res5)
> checkEquals(df1, df5)
[1] TRUE
> 
> ##------------------------------------------
> cat("\n")

> cat("Check caching, with a third ExpressionSet that is almost identical to the first: \n")
Check caching, with a third ExpressionSet that is almost identical to the first: 
> ##------------------------------------------
> esets2 <- c(esets, esets[[1]])
> #newmat <- exprs(esets[[1]])
> #newmat <- newmat + rnorm(nrow(newmat) * ncol(newmat), sd=0.1)
> #ExpressionSet(assayData=exprs(esets[[1]]), phenoData=phenoData(esets[[1]]))
> 
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
> esets2[[3]]$X10 <- "a"
> sampleNames(esets2[[3]]) <- paste("X", sampleNames(esets2[[3]]), sep="")
> 
> tmpcachedir <- tempdir()
> ##Do this twice, so the second time the cache will be used:
> for (i in 1:2){
+     res6 <- doppelgangR(esets2, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=tmpcachedir)
+     ##Make sure comparison of m to n is the same as res1:
+     df6a <- summary(res6)
+     df6a <- df6a[grepl("^[mn]", df6a$sample1) & grepl("^[mn]", df6a$sample2), ]
+     rownames(df6a) <- 1:nrow(df6a)
+     checkEquals(df1, df6a)
+ }
Working on datasets o and o
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and o
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and o
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
Working on datasets o and o
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and m
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and o
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets n and o
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Finalizing...
> 
> df6b <- summary(res6)
> df6b <- df6b[grepl("^[mo]", df6b$sample1) & grepl("^[mo]", df6b$sample2), ]
> checkIdentical(df6b[df6b$sample1=="o:Xm2" & df6b$sample2=="o:Xm3", "expr.doppel"], TRUE)
[1] TRUE
> df6b <- df6b[-1:-2, ]
> ##checkTrue(all(df6b$expr.doppel))  ## not a bug, but a shortcoming in the outlier detection that these are not all identified as expression doppelgangers.
> checkTrue(all(df6b$pheno.doppel))
[1] TRUE
> checkTrue(all(df6b$smokinggun.doppel))
[1] TRUE
> 
> 
> res7 <- doppelgangR(esets2, phenoFinder.args=NULL, smokingGunFinder.args=NULL,
+       outlierFinder.expr.args=list(bonf.prob = 1.0, transFun = atanh,
+           tail = "upper"),    cache.dir=NULL)
Working on datasets o and o
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets m and o
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Working on datasets n and o
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Finalizing...
> df7 <- summary(res7)
> has.o <- grepl("^o", df7$sample1) | grepl("^o", df7$sample2)
> 
> df7a <- df7[has.o, ]
> df7a$sample1 <- sub("o", "m", df7a$sample1)
> df7a$sample1 <- sub("X", "", df7a$sample1)
> df7a$sample2 <- sub("o", "m", df7a$sample2)
> df7a$sample2 <- sub("X", "", df7a$sample2)
> 
> df7b <- df7[!has.o, ]
> df7b <- df7b[(!grepl("^n", df7b$sample1) | !grepl("^n", df7b$sample2)), ]
> for (i in 1:nrow(df7a)){
+     df7a[i, 1:2] <- sort(df7a[i, 1:2])
+     df7b[i, 1:2] <- sort(df7b[i, 1:2])
+ }
> df7a <- df7a[order(df7a$sample1, df7a$sample2), ]
> df7b <- df7b[order(df7b$sample1, df7b$sample2), ]
> checkTrue(all(df7a$expr.doppel == df7b$expr.doppel))
[1] TRUE
> checkTrue(all(df7a$pheno.doppel == df7b$pheno.doppel))
[1] TRUE
> checkTrue(all(df7a$smokinggun.doppel == df7b$smokinggun.doppel))
[1] TRUE
> 
> ##------------------------------------------
> cat("\n")

> cat("Check corFinder function: \n")
Check corFinder function: 
> ##------------------------------------------
> cor1 <- corFinder(eset.pair=esets)
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

> cor2 <- corFinder(eset.pair=esets[c(2, 1)])
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

> checkEquals(cor1, t(cor2))
[1] TRUE
> 
> cor1 <- corFinder(eset.pair=esets, use.ComBat=FALSE)
> cor2 <- corFinder(eset.pair=esets[c(2, 1)], use.ComBat=FALSE)
> checkEquals(cor1, t(cor2))
[1] TRUE
> 
> cor1 <- corFinder(eset.pair=esets[c(1, 1)])
> cor2 <- corFinder(eset.pair=esets[c(1, 1)], use.ComBat=FALSE)
> checkEquals(cor1, cor2)
[1] TRUE
> 
> ##Check missing values:
> exprs(esets[[1]])[1:10, 1:5] <- NA
> doppelgangR(esets[1:2])
KNN imputation for m
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
S4 object of class: DoppelGang 
Number of potential doppelgangers: 7 :  6 expression,  1 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 > ## More missing values:
> exprs(esets[[1]])[1:10, 1:8] <- NA
> doppelgangR(esets[1:2])
KNN imputation for m
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Finalizing...
S4 object of class: DoppelGang 
Number of potential doppelgangers: 7 :  6 expression,  1 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 Warning message:
In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> ## More missing values:
> exprs(esets[[1]])[1:10, 1:11] <- NA
> doppelgangR(esets[1:2])
KNN imputation for m
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Finalizing...
S4 object of class: DoppelGang 
Number of potential doppelgangers: 7 :  6 expression,  1 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 Warning message:
In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> ## infinite values:
> exprs(esets[[1]])[14, 1] <- -Inf
> exprs(esets[[1]])[15, 2] <- Inf
> obs <- tryCatch(doppelgangR(esets[1:2]), warning=conditionMessage)
> checkIdentical("Replacing -+Inf with min/max expression values for dataset m", obs)
[1] TRUE
> 
> ##------------------------------------------
> cat("\n")

> cat("Smoking guns only with cache=TRUE: \n")
Smoking guns only with cache=TRUE: 
> ##------------------------------------------
> dop <- doppelgangR(esets, corFinder.args=NULL, phenoFinder.args=NULL, manual.smokingguns="id")
KNN imputation for m
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets, corFinder.args = NULL, phenoFinder.args = NULL,  :
  Replacing -+Inf with min/max expression values for dataset m
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> checkEquals(summary(dop)[, 1], "m:m5")
[1] TRUE
> checkEquals(summary(dop)[, 2], "n:n4")
[1] TRUE
> 
> 
> ##------------------------------------------
> cat("\n")

> cat("Identical ExpressionSets:\n")
Identical ExpressionSets:
> ##------------------------------------------
> df1 <- summary(doppelgangR(esets[[1]], cache.dir=NULL))
KNN imputation for ExpressionSet1
KNN imputation for ExpressionSet2
Working on datasets ExpressionSet1 and ExpressionSet2
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets[[1]], cache.dir = NULL) :
  Replacing -+Inf with min/max expression values for dataset ExpressionSet1
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
3: In doppelgangR(esets[[1]], cache.dir = NULL) :
  Replacing -+Inf with min/max expression values for dataset ExpressionSet2
4: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> checkTrue(df1$sample1 == "m2")
[1] TRUE
> checkTrue(df1$sample2 == "m3")
[1] TRUE
> ##
> df2 <- summary(doppelgangR(esets[[2]], cache.dir=NULL))
Working on datasets ExpressionSet1 and ExpressionSet2
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> checkTrue(all(df2$sample1 == "n2"))
[1] TRUE
> checkTrue(all(df2$sample2 == "n3"))
[1] TRUE
> ##
> df5 <- summary(doppelgangR(list(eset1=esets[[1]], eset2=esets[[2]]), cache.dir=NULL))
KNN imputation for eset1
Working on datasets eset1 and eset2
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets eset1 and eset1
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets eset2 and eset2
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(list(eset1 = esets[[1]], eset2 = esets[[2]]), cache.dir = NULL) :
  Replacing -+Inf with min/max expression values for dataset eset1
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> df6 <- summary(doppelgangR(esets, cache.dir=NULL))
KNN imputation for m
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets, cache.dir = NULL) :
  Replacing -+Inf with min/max expression values for dataset m
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> checkIdentical(df5[, -1:-2], df6[, -1:-2])
[1] TRUE
> checkIdentical(sub("eset2", "n", sub("eset1", "m", df5$sample1)), df6$sample1)
[1] TRUE
> checkIdentical(sub("eset2", "n", sub("eset1", "m", df5$sample2)), df6$sample2)
[1] TRUE
> 
> ## with zero-column pData:
> withpheno <- summary(doppelgangR(esets))
KNN imputation for m
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets) :
  Replacing -+Inf with min/max expression values for dataset m
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> esets3 <- esets
> pData(esets3[[1]]) <- pData(esets3[[1]])[, 0]
> withoutpheno <- summary(doppelgangR(esets3))
KNN imputation for m
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets3) :
  Replacing -+Inf with min/max expression values for dataset m
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> 
> pData(esets3[[2]]) <- pData(esets3[[2]])[, 0]
> withoutpheno2 <- summary(doppelgangR(esets3))
KNN imputation for m
Working on datasets m and m
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets m and n
Calculating correlations...
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data

Identifying correlation doppelgangers...
Finalizing...
Warning messages:
1: In doppelgangR(esets3) :
  Replacing -+Inf with min/max expression values for dataset m
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> 
> withoutpheno3 <- withoutpheno[!withoutpheno$pheno.doppel, ]
> withoutpheno4 <- withoutpheno2[!withoutpheno2$pheno.doppel, ]
> 
> checkIdentical(withoutpheno[, 1:4], withoutpheno3[, 1:4])
[1] TRUE
> checkIdentical(withoutpheno2[, 1:4], withoutpheno4[, 1:4])
[1] TRUE
> 
> esets4 <- esets
>  for (i in 1:length(esets4))
+     pData(esets4[[i]]) <- pData(esets4[[i]])[1]
> doppelgangR(esets4[[1]])
KNN imputation for ExpressionSet1
KNN imputation for ExpressionSet2
Working on datasets ExpressionSet1 and ExpressionSet2
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
S4 object of class: DoppelGang 
Number of potential doppelgangers: 1 :  1 expression,  0 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 Warning messages:
1: In doppelgangR(esets4[[1]]) :
  Replacing -+Inf with min/max expression values for dataset ExpressionSet1
2: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
3: In doppelgangR(esets4[[1]]) :
  Replacing -+Inf with min/max expression values for dataset ExpressionSet2
4: In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  10 rows with more than 50 % entries missing;
 mean imputation used for these rows
> doppelgangR(esets4[[2]])
Working on datasets ExpressionSet1 and ExpressionSet2
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
S4 object of class: DoppelGang 
Number of potential doppelgangers: 1 :  1 expression,  0 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 > 
> 
> proc.time()
   user  system elapsed 
  9.020   0.832  15.540 

name	user	system	elapsed
corFinder	5.424	0.292	6.423
doppelgangR	3.988	0.168	5.353
dst	0.060	0.000	0.063
mst.mle	0.552	0.000	0.554
outlierFinder	2.272	0.076	2.346
phenoDist	3.848	0.112	3.966
phenoFinder	4.012	0.108	4.125
plot-methods	13.024	0.348	31.235
smokingGunFinder	4.104	0.120	4.224
vectorHammingDist	0.004	0.000	0.001
vectorWeightedDist	0.000	0.000	0.001