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BioC 3.4: CHECK report for doppelgangR on morelia

This page was generated on 2016-09-21 03:55:43 -0700 (Wed, 21 Sep 2016).

Package 332/1257HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
doppelgangR 1.1.2
Levi Waldron
Snapshot Date: 2016-09-19 19:15:14 -0700 (Mon, 19 Sep 2016)
URL: https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/doppelgangR
Last Changed Rev: 117512 / Revision: 121152
Last Changed Date: 2016-05-15 13:14:22 -0700 (Sun, 15 May 2016)
zin1 Linux (Ubuntu 16.04 LTS) / x86_64  NotNeeded  OK  ERROR 
moscato1 Windows Server 2008 R2 Standard (64-bit) / x64  NotNeeded  OK  ERROR  OK 
morelia Mac OS X Mavericks (10.9.5) / x86_64  NotNeeded  OK [ ERROR ] OK 

Summary

Package: doppelgangR
Version: 1.1.2
Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --no-vignettes --timings doppelgangR_1.1.2.tar.gz
StartedAt: 2016-09-20 05:46:35 -0700 (Tue, 20 Sep 2016)
EndedAt: 2016-09-20 05:49:23 -0700 (Tue, 20 Sep 2016)
EllapsedTime: 168.4 seconds
RetCode: 1
Status:  ERROR 
CheckDir: doppelgangR.Rcheck
Warnings: NA

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --no-vignettes --timings doppelgangR_1.1.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.4-bioc/meat/doppelgangR.Rcheck’
* using R version 3.3.1 (2016-06-21)
* using platform: x86_64-apple-darwin13.4.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘doppelgangR/DESCRIPTION’ ... OK
* this is package ‘doppelgangR’ version ‘1.1.2’
* 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 11.156  0.417  30.880
corFinder     6.984  0.199   8.069
doppelgangR   3.917  0.186   8.291
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘maintest.R’
 ERROR
Running the tests in ‘tests/maintest.R’ failed.
Last 13 lines of output:
  > ##------------------------------------------
  > 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]])
  > names(esets2)[3] <- "o"
  > exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
  Error in (function (od, vd)  : 
    object and replacement value dimnames differ
  Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
  Execution halted
* 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: 1 ERROR
See
  ‘/Users/biocbuild/bbs-3.4-bioc/meat/doppelgangR.Rcheck/00check.log’
for details.

maintest.Rout.fail:


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.4.0 (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, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, 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, 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)
> n.pdata <- matrix(letters[sample(1:26, size=100, replace=TRUE)], ncol=10)
> n.pdata[10, ] <- m.pdata[10, ]; npheno <- npheno+1
> ##Create ExpressionSets
> m.eset <- ExpressionSet(assayData=m1)
> m.eset$id <- toupper(colnames(m1))
> pData(m.eset) <- data.frame(c(pData(m.eset), data.frame(m.pdata)))
> ##
> n.eset <- ExpressionSet(assayData=n1)
> n.eset$id <- toupper(colnames(n1))
> ##m5 and n4 are "smoking gun" doppelgangers:
> n.eset$id[4] <- "gotcha"
> m.eset$id[5] <- "gotcha"
> pData(n.eset) <- data.frame(c(pData(n.eset), data.frame(n.pdata)))
> 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...
Found 2 batches
Adjusting for 0 covariate(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$sample1=="m:1" & df1$sample2=="n:1", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:2" & df1$sample2=="m:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="n:2" & df1$sample2=="n:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:10" & df1$sample2=="n:10", "pheno.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:5" & df1$sample2=="n:4", "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 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 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...
Found 2 batches
Adjusting for 0 covariate(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 n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(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...
Found 2 batches
Adjusting for 0 covariate(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]])
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
Error in (function (od, vd)  : 
  object and replacement value dimnames differ
Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
Execution halted

doppelgangR.Rcheck/00install.out:

* 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)

doppelgangR.Rcheck/doppelgangR-Ex.timings:

nameusersystemelapsed
corFinder6.9840.1998.069
doppelgangR3.9170.1868.291
dst0.0100.0020.011
mst.mle0.1290.0120.141
outlierFinder1.7360.0571.793
phenoDist3.1710.0493.221
phenoFinder3.7100.1063.818
plot-methods11.156 0.41730.880
smokingGunFinder3.2140.0533.269
vectorHammingDist0.0010.0000.001
vectorWeightedDist0.0010.0010.001