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### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MLP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MLP_1.54.0.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/MLP.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘MLP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MLP’ version ‘1.54.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MLP’ 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 code 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 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 ... NOTE
checkRd: (-1) getGeneSets.Rd:30-31: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) getGeneSets.Rd:32-33: Lost braces in \itemize; \value handles \item{}{} directly
* 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 sizes of PDF files under ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                         user system elapsed
getGeneSets           127.856  2.945 159.001
addGeneSetDescription 109.310 11.154 128.642
MLP                    40.044  2.111  45.895
plot.MLP                6.024  0.148   6.233
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘test.KEGG.R’
  Running ‘test.MLP.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.20-bioc/meat/MLP.Rcheck/00check.log’
for details.

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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL MLP
###
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* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘MLP’ ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** 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
* DONE (MLP)

R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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(MLP)
> 
> pathPValues <- system.file("exampleFiles", "examplePValues.rda", package = "MLP")
> load(pathPValues)
> 
> pvalues <- examplePValues[seq.int(1000)]
> 
> system.time(geneSet <- getGeneSets(
+ 	species = "Mouse", 
+ 	geneSetSource = "KEGG", 
+ 	entrezIdentifiers = names(pvalues)
+ ))
   user  system elapsed 
 87.044   0.547 112.920 
> 
> set.seed(111)
> mlpOut <- MLP(
+ 	geneSet = geneSet, 
+ 	geneStatistic = pvalues
+ ) 	
> 
> mlpOutWithGeneSetDescr <- addGeneSetDescription(object = mlpOut, geneSetSource = "KEGG")
Warning message:
In addGeneSetDescription(object = mlpOut, geneSetSource = "KEGG") :
  The MLP object already contains a column 'geneSetDescription'
> 
> 	
> 
> proc.time()
   user  system elapsed 
 95.761   1.087 148.989 

R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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.

> require(MLP)	
Loading required package: MLP
> set.seed(479)
> 
> # This is just the expressionset for this experiment.
> 
> pathExampleData <- system.file("exampleFiles", "expressionSetGcrma.rda", package = "MLP")
> load(pathExampleData)
> 
> # Libraries needed
> library(limma)
> library(org.Mm.eg.db) # for mouse
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following object is masked from 'package:limma':

    plotMA

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, aperm, 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, saveRDS, setdiff, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

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

    I, expand.grid, unname


> 
> exprs(expressionSetGcrma)[1:2,]
              2760     2763     2765     2766     2768     2769     2761
100009600 2.371111 2.170060 2.233383 2.180717 2.325886 2.239441 2.297301
100012    2.176163 2.318876 2.419263 2.223307 2.585125 2.346060 2.292061
              2762    2764     2767     2770     2771
100009600 2.409001 2.49458 2.115814 2.371262 2.267459
100012    2.336415 2.47979 2.361981 2.330418 2.520918
> #              2760     2763     2765     2766     2768     2769     2761     2762    2764     2767
> #100009600 2.371111 2.170060 2.233383 2.180717 2.325886 2.239441 2.297301 2.409001 2.49458 2.115814
> #100012    2.176163 2.318876 2.419263 2.223307 2.585125 2.346060 2.292061 2.336415 2.47979 2.361981
> #              2770     2771
> #100009600 2.371262 2.267459
> #100012    2.330418 2.520918
> 
> pData(expressionSetGcrma)
     sample subGroup sampleColor subGroup1
2760      1        1     #FF0000        WT
2763      4        1     #FF0000        WT
2765      6        1     #FF0000        WT
2766      7        1     #FF0000        WT
2768      9        1     #FF0000        WT
2769     10        1     #FF0000        WT
2761      2        2     #0000FF        KO
2762      3        2     #0000FF        KO
2764      5        2     #0000FF        KO
2767      8        2     #0000FF        KO
2770     11        2     #0000FF        KO
2771     12        2     #0000FF        KO
> #     sample subGroup sampleColor
> #2760      1        1     #FF0000
> #2763      4        1     #FF0000
> #2765      6        1     #FF0000
> #2766      7        1     #FF0000
> #2768      9        1     #FF0000
> #2769     10        1     #FF0000
> #2761      2        2     #0000FF
> #2762      3        2     #0000FF
> #2764      5        2     #0000FF
> #2767      8        2     #0000FF
> #2770     11        2     #0000FF
> #2771     12        2     #0000FF
> 
> pData(expressionSetGcrma)$subGroup1 <- ifelse(pData(expressionSetGcrma)$subGroup==1,"WT","KO")
> 
> ###==============================================GENERATING LIMMA p-VALUES=================================
> 
> # boxplot(data.frame(exprs(expressionSetGcrma))
> normDat  <- normalizeQuantiles(exprs(expressionSetGcrma), ties=TRUE)
> subGroup <- pData(expressionSetGcrma)$subGroup
> design <- model.matrix(~ -1 +factor(subGroup ))
> 
> colnames(design) <- c("group1", "group2")
> contrast.matrix <- makeContrasts(group1-group2, levels=design)
> fit <- lmFit(normDat,design)
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit2 <- eBayes(fit2)
> normDat.p <- fit2$p.value
> 
> normDat.p[1:5]
[1] 0.4328583 0.7448996 0.6088859 0.1845008 0.2312761
> #[1] 0.4328583 0.7448996 0.6088859 0.1845008 0.2312761
> 
> system.time(goGeneSet <- getGeneSets(species = "Mouse", geneSetSource = "GOBP", entrezIdentifiers = featureNames(expressionSetGcrma)))
Loading required namespace: GO.db

   user  system elapsed 
 51.460   6.090  61.034 
> goGeneSet[1:3]
$`GO:0000002`
 [1] "11545"  "12628"  "13804"  "16882"  "17258"  "17527"  "18975"  "19819" 
 [9] "20133"  "21975"  "22059"  "23797"  "27393"  "27395"  "27397"  "50776" 
[17] "57813"  "70556"  "72170"  "72962"  "74143"  "74244"  "74528"  "76781" 
[25] "83408"  "83945"  "192287" "208084" "216021" "216860" "226153" "230784"
[33] "276852" "327762" "381760" "382985" "408022"

$`GO:0000012`
 [1] "11545"  "14211"  "21958"  "22064"  "22594"  "66408"  "70099"  "71991" 
 [9] "72103"  "93759"  "104884" "234258" "319583" "319955"

$`GO:0000017`
[1] "20537"  "246787"

> # output changes with annotation version !
> 
> y <- normDat.p[,1]
> names(y) <- featureNames(expressionSetGcrma)
> 
> y[1:10]
100009600    100012    100017    100019 100034251 100036521 100037258 100037278 
0.4328583 0.7448996 0.6088859 0.1845008 0.2312761 0.7865153 0.7772888 0.1037431 
100038570 100038635 
0.1368744 0.3272610 
> # 100009600    100012    100017    100019 100034251 100036521 100037258 100037278 
> # 0.4328583 0.7448996 0.6088859 0.1845008 0.2312761 0.7865153 0.7772888 0.1037431 
> # 100038570 100038635 
> # 0.1368744 0.3272610 
> 
> mlpObject <- MLP(geneSet = goGeneSet, geneStatistic = y, minGenes = 5, maxGenes = 100, rowPermutations = TRUE, 
+     nPermutations = 6, smoothPValues = TRUE)
> 
> 
> 
> mlpObject[1:10, ]
           totalGeneSetSize testedGeneSetSize geneSetStatistic geneSetPValue
GO:0098700               11                 9        1.6077152  9.928398e-06
GO:0002478               34                29        0.9941643  5.186240e-05
GO:0002468               17                12        1.2692571  1.196260e-04
GO:0019884               41                34        0.9093627  1.257672e-04
GO:0019886               22                19        1.0530544  2.086268e-04
GO:0002282                5                 5        1.6379883  2.107123e-04
GO:0048002               87                52        0.7697129  3.498831e-04
GO:0002495               26                23        0.9693229  3.535690e-04
GO:0002504               28                25        0.9087814  7.747836e-04
GO:1905146               21                21        0.9127922  1.420942e-03
                                                                                  geneSetDescription
GO:0098700                                            neurotransmitter loading into synaptic vesicle
GO:0002478                          antigen processing and presentation of exogenous peptide antigen
GO:0002468                                        dendritic cell antigen processing and presentation
GO:0019884                                  antigen processing and presentation of exogenous antigen
GO:0019886         antigen processing and presentation of exogenous peptide antigen via MHC class II
GO:0002282                                    microglial cell activation involved in immune response
GO:0048002                                    antigen processing and presentation of peptide antigen
GO:0002495                   antigen processing and presentation of peptide antigen via MHC class II
GO:0002504 antigen processing and presentation of peptide or polysaccharide antigen via MHC class II
GO:1905146                                                       lysosomal protein catabolic process
> # output changes with annotation version !
> 
> plotGOgraph(object = mlpObject, main = "test of main")
Loading required namespace: Rgraphviz
Loading required namespace: GOstats
Loading required namespace: gplots
> 
> pdf(file = "test10.pdf", width = 10, height = 10)
> # x11(width = 10, height = 10)
> plot(mlpObject, nRow = 10) # by default:  type = "barplot"
> dev.off()
pdf 
  2 
> 
> unlink("test10.pdf")
> 
> if (FALSE){
+   pdf(file = "test5.pdf", width =10, height = 10)
+   mlpBarplot(object = mlpObject, geneSetSource = "GOBP", nRow = 10, descriptionLength = 5)
+   dev.off()
+   
+   unlink("test5.pdf")
+   
+   pdf(file = "test100.pdf", width =10, height = 20)
+   mlpBarplot(object = mlpObject, geneSetSource = "GOBP", nRow = 10, descriptionLength = 100)
+   dev.off()
+   
+   unlink("test100.pdf")
+ }
> 
> plot(mlpObject, type = "quantileCurves")
> plot(mlpObject, type = "GOgraph")
> 
> proc.time()
   user  system elapsed 
 92.727  10.451 107.656