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CHECK report for doppelgangR on tokay2

This page was generated on 2019-10-16 12:35:14 -0400 (Wed, 16 Oct 2019).

Package 457/1741HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
doppelgangR 1.12.0
Levi Waldron
Snapshot Date: 2019-10-15 17:01:26 -0400 (Tue, 15 Oct 2019)
URL: https://git.bioconductor.org/packages/doppelgangR
Branch: RELEASE_3_9
Last Commit: 1df99ea
Last Changed Date: 2019-05-02 11:54:00 -0400 (Thu, 02 May 2019)
malbec2 Linux (Ubuntu 18.04.2 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay2 Windows Server 2012 R2 Standard / x64  OK  OK [ OK ] OK UNNEEDED, same version exists in internal repository
celaya2 OS X 10.11.6 El Capitan / x86_64  OK  OK  OK  OK UNNEEDED, same version exists in internal repository

Summary

Package: doppelgangR
Version: 1.12.0
Command: C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:doppelgangR.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings doppelgangR_1.12.0.tar.gz
StartedAt: 2019-10-16 03:20:19 -0400 (Wed, 16 Oct 2019)
EndedAt: 2019-10-16 03:37:04 -0400 (Wed, 16 Oct 2019)
EllapsedTime: 1004.5 seconds
RetCode: 0
Status:  OK  
CheckDir: doppelgangR.Rcheck
Warnings: 0

Command output

##############################################################################
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###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:doppelgangR.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings doppelgangR_1.12.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.9-bioc/meat/doppelgangR.Rcheck'
* using R version 3.6.1 (2019-07-05)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'doppelgangR/DESCRIPTION' ... OK
* this is package 'doppelgangR' version '1.12.0'
* package encoding: UTF-8
* 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 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
* loading checks for arch 'i386'
** 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
* loading checks for arch 'x64'
** 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 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 files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
              user system elapsed
plot-methods 15.27   0.68   60.80
corFinder     5.16   0.44    5.61
doppelgangR   4.39   0.31   33.31
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
              user system elapsed
plot-methods 12.37   0.74   67.75
corFinder     7.07   0.40    7.49
doppelgangR   5.10   0.24   31.56
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'maintest.R'
 OK
** running tests for arch 'x64' ...
  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

Installation output

doppelgangR.Rcheck/00install.out

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###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.9/bioc/src/contrib/doppelgangR_1.12.0.tar.gz && rm -rf doppelgangR.buildbin-libdir && mkdir doppelgangR.buildbin-libdir && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=doppelgangR.buildbin-libdir doppelgangR_1.12.0.tar.gz && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL doppelgangR_1.12.0.zip && rm doppelgangR_1.12.0.tar.gz doppelgangR_1.12.0.zip
###
##############################################################################
##############################################################################


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 71543  100 71543    0     0   796k      0 --:--:-- --:--:-- --:--:--  852k

install for i386

* installing *source* package 'doppelgangR' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'doppelgangR'
    finding HTML links ... done
    DoppelGang-class                        html  
    colFinder                               html  
    corFinder                               html  
    doppelgangR-package                     html  
    doppelgangR                             html  
    dst                                     html  
    mst.mle                                 html  
    outlierFinder                           html  
    phenoDist                               html  
    phenoFinder                             html  
    plot-methods                            html  
    smokingGunFinder                        html  
    vectorHammingDist                       html  
    vectorWeightedDist                      html  
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'doppelgangR' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'doppelgangR' as doppelgangR_1.12.0.zip
* DONE (doppelgangR)
* installing to library 'C:/Users/biocbuild/bbs-3.9-bioc/R/library'
package 'doppelgangR' successfully unpacked and MD5 sums checked

Tests output

doppelgangR.Rcheck/tests_i386/maintest.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, 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 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...
> 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 m and m
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
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 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...
> 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 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...
Working on datasets n and n
Calculating correlations...
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 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...
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 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 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 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 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 m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
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 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 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...
Working on datasets m and m
Calculating correlations...
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 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...
Working on datasets n and n
	Skipping corFinder, loading cached results.
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 n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and m
Calculating correlations...
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 n and n
Identifying smoking-gun doppelgangers...
Working on datasets m and m
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 eset1
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
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 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...
	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...
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 m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
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 message:
In FUN(...) :
  m and n have different column names in phenoData.  Skipping phenotype checking for this pair.  Set phenoFinger.args=NULL to disable phenotype checking altogether.
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 
  20.78    0.76  256.18

doppelgangR.Rcheck/tests_x64/maintest.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, 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 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...
Working on datasets n and n
Calculating correlations...
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 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...
Working on datasets n 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 m
Identifying smoking-gun doppelgangers...
Working on datasets m and n
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 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...
> 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 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...
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 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 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 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 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 m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
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 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 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...
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 n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
Identifying phenotype doppelgangers...
Working on datasets m and m
Calculating correlations...
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 m and n
Identifying smoking-gun doppelgangers...
Working on datasets n 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 eset1
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
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 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...
	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...
Working on datasets n and n
	Skipping corFinder, loading cached results.
Identifying correlation doppelgangers...
	Skipping phenoFinder, loading cached results.
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 m and m
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets n and n
	Skipping corFinder, loading cached results.
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 message:
In FUN(...) :
  m and n have different column names in phenoData.  Skipping phenotype checking for this pair.  Set phenoFinger.args=NULL to disable phenotype checking altogether.
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 n and n
Calculating correlations...
Identifying correlation doppelgangers...
Working on datasets m and m
	Skipping corFinder, loading cached results.
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 
  21.93    0.54  278.31

Example timings

doppelgangR.Rcheck/examples_i386/doppelgangR-Ex.timings

nameusersystemelapsed
corFinder5.160.445.61
doppelgangR 4.39 0.3133.31
dst000
mst.mle0.220.000.22
outlierFinder3.060.403.47
phenoDist4.360.244.59
phenoFinder3.780.334.11
plot-methods15.27 0.6860.80
smokingGunFinder4.280.144.42
vectorHammingDist000
vectorWeightedDist000

doppelgangR.Rcheck/examples_x64/doppelgangR-Ex.timings

nameusersystemelapsed
corFinder7.070.407.49
doppelgangR 5.10 0.2431.56
dst000
mst.mle0.130.000.12
outlierFinder1.990.202.23
phenoDist3.370.203.58
phenoFinder3.070.223.28
plot-methods12.37 0.7467.75
smokingGunFinder3.190.233.42
vectorHammingDist000
vectorWeightedDist000