Galaxy |

Changeset 0:84361ce36a11 (2016-04-19)

Next changeset 1:a78f84fc4873 (2016-04-19)

Commit message:
planemo upload commit fb90aafc93e5e63acfcdac4c27cfd865cdf06c5a-dirty

diff -r 000000000000 -r 84361ce36a11 README.md
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md Tue Apr 19 11:10:25 2016 -0400

[

b'@@ -0,0 +1,146 @@\n+Minfi (for galaxy)\n+====\n+\n+*Maintainer: IUC*\n+\n+*Contact: nitesh1989 on github*\n+\n+The minfi package provides tools for analyzing Illumina\xe2\x80\x99s Methylation arrays, with a special\n+focus on the new 450k array for humans.\n+\n+In this package we refer to differentially methylated positions (DMPs) by which we mean\n+a single genomic position that has a different methylation level in two different groups of\n+samples (or conditions). This is different from differentially methylated regions (DMRs)\n+which imply more that more than one methylation positions are different between conditions.\n+\n+### Table of Contents\n+**[User Guides](#user-guides)** \n+**[Functions provided and description](#functions-provided-and-description)** \n+**[Minfi Pipeline to analyze illumina 450k data](#minfi-pipeline-to-analyze-illumina-450k-data)** \n+**[Minfi Analysis Pipeline for TCGA data hosted on GDAC Broad Institute](#minfi-analysis-pipeline-for-tcga-data-hosted-on-gdac-broad-institute)** \n+**[Test Data](#test-data)** \n+\n+\n+##User Guides\n+Here are some excellent user guides about minfi, written by the contributors.\n+\n+http://www.bioconductor.org/packages/release/bioc/vignettes/minfi/inst/doc/minfi.pdf\n+\n+https://bioconductor.org/help/course-materials/2014/BioC2014/minfi_BioC2014.pdf\n+\n+https://www.bioconductor.org/help/course-materials/2015/BioC2015/methylation450k.html\n+\n+##Functions provided and description\n+\n+1. **Minfi** **Pipeline** to analyze illumina 450k data. (minfi_pipeline.xml)\n+\n+2. **Minfi** **Analysis** **pipeline** for TCGA data hosted on GDAC-Broad Institute.(minfi_TCGA_pipeline.xml) \n+\n+\n+##Minfi Pipeline to analyze illumina 450k data \n+\n+####Tool xml name: *minfi_pipeline.xml* \n+\n+####How to use:\n+\n+\n+* The first step is to upload Illumina 450k IDAT files using the Galaxy Upload feature.\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-07%20at%205.43.07%20PM.png" >\n+\n+* In this example, we uploaded "Case" samples from slide 572364052 - one Illumina slide with 6 IDAT files (3 samples with red/green channel pairs) - and "Control" samples from slide 572364053 - one Illumina slide with 6 IDAT files (3 samples with red/green channel pairs) - for a total of 12 IDAT files. Once uploaded, these files should appear in the history panel. \n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-07%20at%205.43.41%20PM.png">\n+\n+* The next step is to group samples based on treatment type. First, select "Operations on multiple datasets" in your history panel.\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-07%20at%205.44.32%20PM.png" width="150">\n+\n+* Next, select the samples which belong to the "Case" treatment group (572364052_*.idat), and "For all selected", "Build a list of dataset pairs". \n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-07%20at%205.45.18%20PM.png" width="150">\n+\n+* The "Create a collection of paired datasets" window will appear with the samples you selected. To pair the red/green channels, first "Clear Filters" and then type each dataset suffix - "_Grn.idat" and "_Red.idat" - in the filter bars. This should filter your list and display the "Green" channel files on one side and "Red" channel files on the other. If the datasets are correctly paied, click "Pair these datasets" in the middle of the window for each pair. Finally, name the collection - in this example "Case" based on the treatement type - and click "Create list".\n+\n+![pairCollection](https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-07%20at%205.46.59%20PM.png)\n+\n+* Repeat these steps to build a paired dataset for the "Control" samples (572364053_*.idat).\n+\n+<img src="https://github.com/nitesh1'..b'alysis Pipeline for TCGA data hosted on GDAC Broad Institute \n+\n+####Tool xml name: *minfi_TCGA_pipeline.xml* \n+\n+####How to use:\n+\n+The minfi analysis pipeline for TCGA data provides only two functions as of now, to find differentially methylated regions and differentially methylated positions. This is because the data which is provided/used for this tool has been put through quality control measures. The Level 3 data is ready to use for analysis. \n+\n+* The first step of this process is to fetch TCGA data from http://gdac.broadinstitute.org/runs/ where we need Standard data from a particular date, or the latest data which is available at http://gdac.broadinstitute.org/runs/stddata__latest/. We want the "Open" data avaiable to download.\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-09%20at%202.42.17%20PM.png" width="300">\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-09%20at%202.42.42%20PM.png">\n+\n+\n+* Choose the cancer type with the Illumina 450K methylation data, for the sake of this example we will use "UCEC" - Uterine Corpus Endometrial Carcinoma. In the directory structure, we want the data with the suffix ```__Level_3__within_bioassay_data_set_function__data.Level_3```. This file is usually the largest in the directory and can be easily spotted by sorting the directory based on Size. So for the example, we will choose the file ```gdac.broadinstitute.org_UCEC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2015110100.0.0.tar.gz```. \n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-09%20at%202.41.05%20PM.png">\n+\n+* There are two options to obtain the data: 1. Get the link to that file and paste it in your Galaxy Upload tool to fetch the data; 2. Download the data on to your local machine and upload the whole file.\n+\n+Option 1 : Fetch the data at the URL directly to Galaxy\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-11%20at%204.30.15%20PM.png">\n+\n+Option 2: Upload a local file\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-11%20at%204.34.30%20PM.png">\n+\n+* Once the upload is finished, it should be available in the History panel of the session. Choose the tool "Minfi Analysis pipeline for TCGA data". \n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-12%20at%205.17.44%20PM.png">\n+\n+\n+* Galaxy will uncompress your dataset to show you only a ".tar" file. Choose that file as the input to the tool. This example will show the tool run with the Basic Default settings.\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-11%20at%204.34.53%20PM.png">\n+\n+* After the tool is executed, two results will appear in the history panel.\n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-11%20at%204.59.32%20PM.png" width="150">\n+\n+* This shows the two results "Differentially methylated Regions" and "Differentially methylated Positions". You can ```View``` the results by clicking on the ```eye``` icon. \n+\n+<img src="https://github.com/nitesh1989/tools-iuc/blob/methylation_2/tools/minfi/help/help-images/Screen%20Shot%202015-12-11%20at%204.59.59%20PM.png">\n+\n+\n+##Test Data\n+\n+The test_data folder contains some test files which are,\n+\n+1. 572364052 : One Illumina slide with 6 IDAT files (3 samples with red/green channel pairs)\n+\n+2. 572364053 : One Illumina slide with 6 IDAT files (3 samples with red/green channel pairs)\n+\n+3. dmrs.csv : Differentially methylated regions as output.\n'

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diff -r 000000000000 -r 84361ce36a11 help/help-scripts/.Rhistory
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/help/help-scripts/.Rhistory Tue Apr 19 11:10:25 2016 -0400

[

@@ -0,0 +1,26 @@
+packageExpand = function(packagelist,fl) {
+res = NULL
+for (i in c(1:length(packagelist))) {
+s = packagelist[i]
+ls = nchar(s)
+spos = which(substr(fl,1,ls) == s,arr.ind=T)
+lspos = length(spos)
+if (lspos > 0)
+{
+fullname = fl[spos]
+if (grepl('*.gz',fullname)) {
+row = paste(ps,fullname,pe,sep='')
+res = append(res,row)
+}
+}
+}
+return(res)
+}
+getPackages = function(packs)
+{
+packages = unlist(tools::package_dependencies(packs, available.packages(),
+which=c("Depends", "Imports"), recursive=TRUE))
+packages = union(packs, packages)
+packages
+}
+packageExpand(c("minfi"))

diff -r 000000000000 -r 84361ce36a11 help/help-scripts/minfi_latest_tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/help/help-scripts/minfi_latest_tool_dependencies.xml Tue Apr 19 11:10:25 2016 -0400

@@ -0,0 +1,57 @@
+<package>https://bioarchive.galaxyproject.org/BiocGenerics_0.16.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/Biobase_2.30.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/S4Vectors_0.8.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/IRanges_2.4.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/GenomeInfoDb_1.6.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/zlibbioc_1.16.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/XVector_0.10.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/GenomicRanges_1.22.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/Biostrings_2.38.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/iterators_1.0.8.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/foreach_1.4.3.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/locfit_1.5-9.1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/matrixStats_0.14.2.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/limma_3.26.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/registry_0.3.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/digest_0.6.8.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/stringi_0.5-5.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/magrittr_1.5.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/stringr_1.0.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/xtable_1.7-4.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/pkgmaker_0.22.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/rngtools_1.2.4.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/doRNG_1.6.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/DBI_0.3.1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/RSQLite_1.0.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/AnnotationDbi_1.32.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/bitops_1.0-6.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/RCurl_1.95-4.7.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/XML_3.98-1.3.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/lambda.r_1.1.7.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/futile.options_1.0.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/futile.logger_1.4.1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/snow_0.3-13.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/BiocParallel_1.3.54.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/Rsamtools_1.22.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/GenomicAlignments_1.6.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/rtracklayer_1.30.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/biomaRt_2.26.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/GenomicFeatures_1.22.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/bumphunter_1.10.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/beanplot_1.2.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/RColorBrewer_1.1-2.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/nor1mix_1.2-1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/multtest_2.26.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/siggenes_1.44.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/preprocessCore_1.32.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/base64_1.1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/illuminaio_0.12.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/mclust_5.0.2.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/annotate_1.48.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/genefilter_1.52.0.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/Rcpp_0.12.1.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/plyr_1.8.3.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/reshape_0.8.5.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/quadprog_1.5-5.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/GEOquery_2.35.7.tar.gz</package>
+<package>https://bioarchive.galaxyproject.org/minfi_1.16.0.tar.gz</package>

diff -r 000000000000 -r 84361ce36a11 help/help-scripts/r_dep_gen.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/help/help-scripts/r_dep_gen.R Tue Apr 19 11:10:25 2016 -0400

[

@@ -0,0 +1,92 @@
+ packageExpand = function(packagelist,fl) {
+   res = NULL
+   for (i in c(1:length(packagelist))) {
+     s = packagelist[i]
+     ls = nchar(s)
+     spos = which(substr(fl,1,ls) == s,arr.ind=T)
+     lspos = length(spos)
+     if (lspos > 0)
+       {
+       fullname = fl[spos]
+       if (grepl('*.gz',fullname)) {
+            row = paste(ps,fullname,pe,sep='')
+            res = append(res,row)
+            }
+       }
+     }
+   return(res)
+ }
+
+ getPackages = function(packs)
+   {
+   packages = unlist(tools::package_dependencies(packs, available.packages(),
+         which=c("Depends", "Imports"), recursive=TRUE))
+   packages = union(packs, packages)
+   packages
+   }
+
+ ourargs = commandArgs(TRUE)
+ if(length(ourargs)==0){
+       print("No arguments supplied.")
+    }else{
+    for(i in 1:length(ourargs)){
+        eval(parse(text=ourargs[[i]]))
+    }
+ }
+
+ unesc = function(x) {
+   res = x
+   res = gsub('__lt__','<',res)
+   res = gsub('__gt__','>',res)
+   return(res)
+ }
+
+ our_packages = strsplit(ourpackages," ")[[1]]
+ ps=unesc(xmlprefix)
+ pe="?raw=true</package>"
+
+ print(paste('tardir=',tardir,'xmlprefix=',xmlprefix,'ourpackages=',ourpackages,'OUTPATH=',OUTPATH))
+
+
+ setRepositories(ind=1:2)
+ chooseBioCmirror(ind=7,graphics=F) # canberra - use eg 1 for FredHutch
+ chooseCRANmirror(ind=5,graphics=F) # Melbourne - use 96 for texas
+
+ ifreq = function(pkg='DESeq2') {
+ if(require(package=pkg,character.only = T)){
+   print(paste(pkg,"is loaded correctly"))
+ } else {
+   print(paste("trying to install",pkg))
+   install.packages(pkg)
+   if(require(package=pkg,character.only = T)){
+   print(paste(pkg,"installed and loaded correctly"))
+   } else {
+     stop(paste("Could not install",pkg))
+   }
+ }
+ }
+
+ ifreq(pkg="BiocInstaller")
+ ifreq(pkg="pkgDepTools")
+ ifreq(pkg="Biobase")
+
+ print.noquote('Greetings! The R you have chosen is using the following repositories:')
+ print.noquote(biocinstallRepos())
+ packages = getPackages(our_packages)
+ download.packages(pkgs=packages,destdir=tardir, type="source", repos=biocinstallRepos())
+ flist = list.files(tardir)
+ allDeps = makeDepGraph(biocinstallRepos(), type="source", keep.builtin=F, dosize=F)
+ res = NULL
+ for (i in c(1:length(our_packages))) {
+   package = our_packages[i]
+   io = getInstallOrder(package, allDeps, needed.only=FALSE)
+   ares = packageExpand(packagelist=io$packages,fl=flist)
+   res = append(res,ares)
+   }
+ ures = unique(res)
+ tout = "savedeps.xml"
+ write.table(ures,file=tout,quote=F,sep="\t",row.names=F,col.names=F)
+ write.table(ures,file=OUTPATH,quote=F,sep="\t",row.names=F,col.names=F)
+ print.noquote(res)
+ sessionInfo()
+ print.noquote(date())
\ No newline at end of file

diff -r 000000000000 -r 84361ce36a11 help/minfi_pipeline_test.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/help/minfi_pipeline_test.R Tue Apr 19 11:10:25 2016 -0400

[

@@ -0,0 +1,232 @@
+# setup R error handling to go to stderr
+options(show.error.messages=F, error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})
+
+# we need that to not crash galaxy with an UTF8 error on German LC settings.
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+
+library("getopt")
+options(stringAsfactors = FALSE, useFancyQuotes = FALSE)
+args <- commandArgs(trailingOnly = TRUE)
+
+# get options, using the spec as defined by the enclosed list.
+# we read the options from the default: commandArgs(TRUE).
+spec <- matrix(c(
+    'quiet', 'q', 2, "logical",
+    'help' , 'h', 0, "logical",
+    "preprocess","p",1,"character",
+    "numPositions","n",2,"integer",
+    "shrinkVar","s",2,"logical",
+    "b_permutations","b",2,"integer",
+    "smooth","m",2,"logical",
+    "cutoff","t",2,"float",
+    "l_value","l",2,"integer",
+    "cores","c",1,"integer")
+    ,byrow=TRUE, ncol=4)
+opt <- getopt(spec)
+
+
+# If help was asked for print a friendly message
+# and exit with a non-zero error code
+if (!is.null(opt$help)) {
+    cat(getopt(spec, usage=TRUE))
+    q(status=1)
+}
+
+
+## Set verbose mode
+verbose = if(is.null(opt$quiet)){TRUE}else{FALSE}
+if(verbose){
+    cat("Verbose mode is ON\n\n")
+}
+
+# Enforce the following required arguments
+if (is.null(opt$preprocess)) {
+    cat("'preprocess' is required\n")
+    q(status=1)
+}
+
+# Load required libraries
+
+suppressPackageStartupMessages({
+    library("minfi")
+    library("FlowSorted.Blood.450k")
+    library("doParallel")
+    library("TxDb.Hsapiens.UCSC.hg19.knownGene")
+})
+
+
+## Parse cheetah code and make dataframe for creating tmp dir
+minfi_config_file = "/Users/nturaga/Documents/workspace/minfi_galaxy/galaxy/database/job_working_directory/000/43/minfi_temp/minfi_config.txt"
+minfi_config = read.table(minfi_config_file)
+colnames(minfi_config) = c("status","green","red","name")
+
+if ( verbose ) {
+    cat("Minfi configuration file:\n\n ");
+    print(minfi_config)
+}
+
+## Make the tmpdir for symlinking data
+base_dir = paste0("/Users/nturaga/Documents/workspace/minfi_galaxy/galaxy/database/job_working_directory/000/73/minfi_temp","/base")
+
+
+### Make symlinks of files
+#for (i in 1:nrow(minfi_config)){
+    #stopifnot(nrow(minfi_config) == nrow(minfi_config["name"]))
+
+    ### Make green idat file symlinks
+    #file_green = paste0(base_dir,"/",as.character(minfi_config[i,"name"]),"_Grn.idat")
+    #cmd_green = paste("ln -s",as.character(minfi_config[i,"green"]),file_green,sep=" ")
+    #cat("Reading file ",i,"GREEN Channel ", file_green)
+    #system(cmd_green)
+
+    ### Make red idat file symlinks
+    #file_red = paste0(base_dir,"/",as.character(minfi_config[i,"name"]),"_Red.idat")
+    #cmd_red = paste("ln -s",as.character(minfi_config[i,"red"]),file_red,sep=" ")
+    #cat("Reading file ",i,"RED Channel ", file_red)
+    #system(cmd_red)
+#}
+
+## Make dataframe with Basenames
+Basename = paste0(base_dir,"/",unique(substr(list.files(base_dir),1,17)))
+status = minfi_config[match(gsub(".+/","",Basename), minfi_config$name),"status"]
+targets = data.frame(Basename=Basename,status=status)
+
+if ( verbose ) {
+    cat("Minfi targets file:\n\n ")
+    print(targets)
+}
+
+## Read 450k files
+RGset = read.450k.exp(targets=targets,verbose=FALSE)
+
+if (verbose){
+    cat("RGset has been read: \n\n")
+    print(RGset)
+}
+
+
+## Preprocess data with the normalization method chosen
+if(opt$preprocess == "quantile"){
+    normalized_RGset = preprocessQuantile(RGset)
+    if (verbose){cat("Preprocessed using Quantile normalization")};
+} else if (opt$preprocess == "noob"){
+    normalized_RGset = preprocessNoob(RGset)
+    if (verbose){cat("Preprocessed using Noob normalization")};
+} else if (opt$preprocess == "raw"){
+    normalized_RGset = preprocessRaw(RGset)
+    if (verbose){print("Preprocessed using Raw normalization")};
+} else if (opt$preprocess == "illumina"){
+    normalized_RGset = preprocessIllumina(RGset,bg.correct = TRUE, normalize = c("controls", "no"),reference = 1)
+    if(verbose){print("Preprocessed using Illumina normalization")}
+} else if (opt$preprocess == "preprocessFunnorm"){
+    normalized_RGset = preprocessFunnorm(RGset)
+    if(verbose){print("Preprocessed using Functional normalization")}
+} else {
+    normalized_RGset = RGset
+    if(verbose){print("Preprocessed using NO normalization")}
+}
+
+
+## Get beta values from Proprocessed data
+beta = getBeta(normalized_RGset)
+## Set phenotype data
+pd=pData(normalized_RGset)
+
+
+## QC REPORT
+files = gsub(".+/","",pd$filenames)
+## Produce PDF file
+if (!is.null(RGset)) {
+    # Make PDF of QC report
+    minfi::qcReport(rgSet=RGset,sampNames=files,sampGroups=pd$status,pdf="qc_report.pdf")
+}
+
+## MDS Plot
+## Set phenotype data
+files = gsub(".+/","",pd$filenames)
+
+## Produce PDF file
+if (!is.null(RGset)) {
+    ## Make PDF of density plot
+    pdf("mds_plot.pdf")
+    minfi::mdsPlot(dat=RGset,sampNames=files,sampGroups=pd$status,main="Beta MDS",numPositions = opt$numPositions,pch=19)
+    dev.off()
+}
+
+
+if(verbose){
+    cat("Made plot of QC report and MDS plot\n\n")
+}
+
+
+ #Estimate Cell counts
+#if(!is.null(RGset)){
+    #cell_counts = minfi::estimateCellCounts(rgSet=RGset,meanPlot=TRUE)
+    #write.csv(cell_counts,file="estimated_cell_counts.csv",quote=FALSE,row.names=TRUE)
+#}
+#if(verbose){
+    #cat("Cell Counts estimated\n\n")
+#}
+
+## DMP finder
+dmp = dmpFinder(dat=beta,pheno=pd$status,type="categorical",shrinkVar=opt$shrinkVar)
+write.csv(dmp,file="dmps.csv",quote=FALSE,row.names=TRUE)
+if(verbose){
+    cat("DMP Finder successful \n")
+}
+
+
+# Model Matrix to pass into the bumphunter function
+pd=pData(normalized_RGset)
+T1= levels(pd$status)[2]
+T2= levels(pd$status)[1]
+
+stopifnot(T1!=T2)
+keep=pd$status%in%c(T1,T2)
+tt=factor(pd$status[keep],c(T1,T2))
+design=model.matrix(~tt)
+
+if(verbose){
+    cat("Model matrix is: \n")
+    design
+}
+
+# Start bumphunter in a parallel environment
+# Parallelize over cores on machine
+registerDoParallel(cores = opt$cores)
+
+## Bumphunter Run with normalized_RGset processed with Quantile Normalization
+
+res=bumphunter(normalized_RGset[,keep],design,B=opt$b_permutations,smooth=opt$smooth,cutoff= opt$cutoff,type="Beta")
+bumps= res$tab
+
+if(verbose){
+    cat("Bumphunter result", "\n")
+    head(bumps)
+}
+
+## Choose DMR's of a certain length threshold.
+## This helps reduce the size of DMRs early, and match
+## with genes closest to region
+bumps = bumps[bumps$L>opt$l_value,]
+genes <- annotateTranscripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
+tab=matchGenes(bumps,genes)
+result=cbind(bumps,tab)
+
+if(verbose){
+    cat("Match with annotation\n")
+    head(result)
+}
+
+# Save result, which contains DMR's and closest genes
+write.csv(result,file = "dmrs.csv",quote=FALSE,row.names=TRUE)
+
+# Garbage collect
+gc()
+
+# Block finder
+#library(sva)
+#pheno <- pData(GRset)
+#mod <- model.matrix(~as.factor(status), data=pheno)
+#mod0 <- model.matrix(~1, data=pheno)
+#sva.results <- sva(mval, mod, mod0)

diff -r 000000000000 -r 84361ce36a11 minfi_TCGA_pipeline.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/minfi_TCGA_pipeline.R Tue Apr 19 11:10:25 2016 -0400

[

@@ -0,0 +1,153 @@
+# setup R error handling to go to stderr
+options(show.error.messages=F, error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})
+
+# we need that to not crash galaxy with an UTF8 error on German LC settings.
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+
+library("getopt")
+options(stringAsfactors = FALSE, useFancyQuotes = FALSE)
+args <- commandArgs(trailingOnly = TRUE)
+
+# get options, using the spec as defined by the enclosed list.
+# we read the options from the default: commandArgs(TRUE).
+spec <- matrix(c(
+    'quiet', 'q', 2, "logical",
+    'help' , 'h', 0, "logical",
+    'tarfile','f',1,"character",
+    "cores","c",1,"integer",
+    "b_permutations","b",2,"integer",
+    "smooth","m",2,"logical",
+    "l_value","l",2,"integer")
+    ,byrow=TRUE, ncol=4)
+opt <- getopt(spec)
+
+## If help was asked for print a friendly message
+## and exit with a non-zero error code
+if (!is.null(opt$help)) {
+    cat(getopt(spec, usage=TRUE))
+    q(status=1)
+}
+
+
+## Set verbose mode
+verbose = if(is.null(opt$quiet)){TRUE}else{FALSE}
+if(verbose){
+    cat("Verbose mode is ON\n\n")
+}
+
+## Load required libraries
+suppressPackageStartupMessages({
+    library("minfi")
+    library("FlowSorted.Blood.450k")
+    library("TxDb.Hsapiens.UCSC.hg19.knownGene")
+    library("doParallel")
+    library("tools")
+})
+
+
+config_file = "tcga_temp/config.txt"
+conf = read.csv(config_file,stringsAsFactors=FALSE,header=F)
+tarfile_name = gsub(".+/","",conf$V2)
+dataset_path = conf$V1
+
+cmd = paste("ln -s",dataset_path,tarfile_name,sep=" ")
+cat("Command : ", cmd,"\n")
+system(cmd)
+
+tarfile = tarfile_name
+cat ("tarfile name: ",tarfile," file ext: ",file_ext(tarfile))
+## UNtar files in R first
+if (file_ext(tarfile) == "tar"){
+    cat("Entering IF statment")
+    tar_contents = untar(tarfile,list=TRUE)
+    cat("regex failing here")
+    f = as.character(tar_contents[grep(".data.txt",fixed=TRUE,x=tar_contents)])
+    if (!is.null(f)){
+        cat("Untar being attempted")
+        untar(tarfile,  exdir = ".",files=f)
+        cat("Untar succcess")
+    }
+}
+## Move file from sub directory to main directory
+from = list.files(pattern=".data.txt",recursive=TRUE)
+to = gsub(".+/","",from)
+rename_success = file.rename(from=from, to=to)
+
+
+# This should pass only if steps have been successful
+stopifnot(rename_success)
+if (rename_success){
+    input_file = to
+}
+
+### Read the TCGA data
+GRset = readTCGA(input_file, sep = "\t", keyName = "Composite Element REF", Betaname = "Beta_value", pData = NULL, array = "IlluminaHumanMethylation450k",showProgress=TRUE)
+
+### Get beta values
+beta = getBeta(GRset)
+pd = pData(GRset)
+CN = getCN(GRset)
+chr = seqnames(GRset)
+pos = start(GRset)
+chr = as.character(chr)
+
+
+# Assign phenotype information
+## Based on TCGA sample naming, TCGA-2E-A9G8-01A-11D-A409-05, char 14,15 represent
+## phenotypic status of sample, 01 = cancer, 11=normal
+pd$status = ifelse(test= (substr(rownames(pd),14,15) == "01"),yes="cancer",no="normal")
+
+### DMP finder
+dmp = dmpFinder(dat=beta,pheno=pd$status,type="categorical")
+write.csv(dmp,file="dmps.csv",quote=FALSE,row.names=TRUE)
+if(verbose){
+    cat("DMP Finder successful \n")
+}
+
+
+## Make design matrix for bumphunting
+#Model Matrix
+T1="normal";T2="cancer"
+## Introduce error if levels are different
+stopifnot(T1!=T2)
+keep=pd$status%in%c(T1,T2)
+tt=factor(pd$status[keep],c(T1,T2))
+design=model.matrix(~tt)
+
+
+## Start bumphunter in a parallel environment
+## Parallelize over cores on machine
+library(doParallel)
+registerDoParallel(cores = opt$cores)
+
+# Bumphunter Run with object processed with default Quantile Normalization
+# provided along with TCGA data
+dmrs = bumphunter(beta[,keep],chr=chr,pos=pos,design=design,B=opt$b_permutations,smooth=opt$smooth,pickCutoff =TRUE)
+bumps = dmrs$tab
+
+if(verbose){
+    cat("Bumphunter result", "\n")
+    head(bumps)
+}
+
+
+### Choose DMR's of a certain length threshold.
+### This helps reduce the size of DMRs early, and match
+### with genes closest to region
+bumps = bumps[bumps$L > opt$l_value,]
+genes <- annotateTranscripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
+tab=matchGenes(bumps,genes)
+annotated_dmrs=cbind(bumps,tab)
+
+if(verbose){
+    cat("Match with annotation\n")
+    head(annotated_dmrs)
+}
+
+## Save result, which contains DMR's and closest genes
+write.csv(annotated_dmrs,file = "dmrs.csv",quote=FALSE,row.names=FALSE)
+
+## Garbage collect
+gc()
+
+

diff -r 000000000000 -r 84361ce36a11 minfi_macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/minfi_macros.xml Tue Apr 19 11:10:25 2016 -0400

@@ -0,0 +1,14 @@
+<macros>
+    <xml name="requirements">
+        <requirements>
+        <requirement type="package" version="3.2.1">R</requirement>
+         <requirement type="package" version="1.16.0">minfi</requirement>
+        </requirements>
+    </xml>
+ <xml name="citations">
+     <citations>
+     <citation type="doi">10.1093/bioinformatics/btu049</citation>
+         <citation type="doi">10.1093/ije/dyr238</citation>
+        </citations>
+    </xml>
+</macros>

diff -r 000000000000 -r 84361ce36a11 minfi_pipeline.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/minfi_pipeline.R Tue Apr 19 11:10:25 2016 -0400

[

@@ -0,0 +1,249 @@
+# setup R error handling to go to stderr
+options(show.error.messages=F, error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})
+
+# we need that to not crash galaxy with an UTF8 error on German LC settings.
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+
+library("getopt")
+options(stringAsfactors = FALSE, useFancyQuotes = FALSE)
+args <- commandArgs(trailingOnly = TRUE)
+
+# get options, using the spec as defined by the enclosed list.
+# we read the options from the default: commandArgs(TRUE).
+spec <- matrix(c(
+    'quiet', 'q', 2, "logical",
+    'help' , 'h', 0, "logical",
+    "preprocess","p",1,"character",
+    "cores","c",1,"integer",
+    "numPositions","n",2,"integer",
+    "shrinkVar","s",2,"logical",
+    "b_permutations","b",2,"integer",
+    "smooth","m",2,"logical",
+    "cutoff","t",2,"double",
+    "l_value","l",2,"integer")
+    ,byrow=TRUE, ncol=4)
+opt <- getopt(spec)
+
+# If help was asked for print a friendly message
+# and exit with a non-zero error code
+if (!is.null(opt$help)) {
+    cat(getopt(spec, usage=TRUE))
+    q(status=1)
+}
+
+
+## Set verbose mode
+verbose = if(is.null(opt$quiet)){TRUE}else{FALSE}
+if(verbose){
+    cat("Verbose mode is ON\n\n")
+}
+
+# Enforce the following required arguments
+if (is.null(opt$preprocess)) {
+    cat("'--preprocess' is required\n")
+    q(status=1)
+}
+cat("verbose = ", opt$quiet,"\n")
+cat("preprocess = ",opt$preprocess,"\n")
+cat("cores = ", opt$cores, "\n")
+cat("b_permutations = ",opt$b_permutations,"\n")
+cat("smooth = ",opt$smooth,"\n")
+cat("cutoff = ",opt$cutoff,"\n")
+cat("l_value = ",opt$l_value,"\n")
+cat("numPositions = ",opt$numPositions,"\n")
+cat("shrinkVar = ",opt$shrinkVar,"\n")
+
+
+# Load required libraries
+suppressPackageStartupMessages({
+    library("minfi")
+    library("FlowSorted.Blood.450k")
+    library("TxDb.Hsapiens.UCSC.hg19.knownGene")
+    library("doParallel")
+})
+
+
+## Parse cheetah code and make dataframe for creating tmp dir
+minfi_config_file = paste0("minfi_temp","/minfi_config.txt")
+minfi_config = read.table(minfi_config_file)
+colnames(minfi_config) = c("status","green","red","name")
+
+
+## Make the tmpdir for symlinking data
+base_dir = paste0("minfi_temp","/base")
+system(paste0("mkdir ",base_dir))
+
+
+## Make symlinks of files
+for (i in 1:nrow(minfi_config)){
+    stopifnot(nrow(minfi_config) == nrow(minfi_config["name"]))
+
+    ## Make green idat file symlinks
+    file_green = paste0(base_dir,"/",as.character(minfi_config[i,"name"]),"_Grn.idat")
+    cmd_green = paste("ln -s",as.character(minfi_config[i,"green"]),file_green,sep=" ")
+    cat("Reading file ",i,"GREEN Channel ", file_green)
+    system(cmd_green)
+
+    ## Make red idat file symlinks
+    file_red = paste0(base_dir,"/",as.character(minfi_config[i,"name"]),"_Red.idat")
+    cmd_red = paste("ln -s",as.character(minfi_config[i,"red"]),file_red,sep=" ")
+    cat("Reading file ",i,"RED Channel ", file_red)
+    system(cmd_red)
+}
+
+## Make dataframe with Basenames
+Basename = paste0(base_dir,"/",unique(substr(list.files(base_dir),1,17)))
+status = minfi_config[match(gsub(".+/","",Basename), minfi_config$name),"status"]
+targets = data.frame(Basename=Basename,status=status)
+
+if ( verbose ) {
+    cat("Minfi targets file:\n\n ")
+    print(targets)
+}
+
+## Read 450k files
+RGset = read.450k.exp(targets=targets,verbose=T)
+
+if (verbose){
+    cat("RGset has been read: \n\n")
+    print(RGset)
+}
+
+pd = pData(RGset)
+
+## NOTE: QC report is for samples before normalization
+## QC REPORT
+files = gsub(".+/","",pd$filenames)
+## Produce PDF file
+if (!is.null(RGset)) {
+    # Make PDF of QC report
+    minfi::qcReport(rgSet=RGset,sampNames=files,sampGroups=pd$status,pdf="qc_report.pdf")
+}
+
+
+## MDS Plot
+## Set phenotype data
+files = gsub(".+/","",pd$filenames)
+#numPositions=as.integer("${numPositions}")
+
+## Produce PDF file
+if (!is.null(RGset)) {
+    ## Make PDF of MDS plot
+    pdf("mds_plot.pdf")
+    minfi::mdsPlot(dat=RGset,sampNames=files,sampGroups=pd$status,main="Beta MDS",numPositions = opt$numPositions,pch=19)
+    dev.off()
+}
+
+if(verbose){
+    cat("Made plot of QC report and MDS plot\n\n")
+}
+
+
+## Preprocess data with the normalization method chosen
+if(opt$preprocess == "quantile"){
+    normalized_RGset = preprocessQuantile(RGset)
+    if (verbose){cat("Preprocessed using Quantile normalization")};
+} else if (opt$preprocess == "funnorm"){
+    normalized_RGset = preprocessFunnorm(RGset)
+    if(verbose){print("Preprocessed using Functional normalization")}
+} else if (opt$preprocess == "noob"){
+    normalized_RGset = preprocessNoob(RGset)
+    if (verbose){cat("Preprocessed using Noob normalization")};
+} else if (opt$preprocess == "illumina"){
+    normalized_RGset = preprocessIllumina(RGset,bg.correct = TRUE, normalize = c("controls", "no"),reference = 1)
+    if(verbose){print("Preprocessed using Illumina normalization")}
+} else if (opt$preprocess == "swan"){
+    normalized_RGset = preprocessSWAN(RGset)
+    if(verbose){print("Preprocessed using SWAN normalization")}
+}else {
+    normalized_RGset = RGset
+    if(verbose){print("Preprocessed using No normalization")}
+}
+
+
+
+## Get beta values from Proprocessed data
+beta = getBeta(normalized_RGset)
+## Set phenotype data
+pd = pData(normalized_RGset)
+
+
+## DMP finder
+dmp = dmpFinder(dat=beta,pheno=pd$status,type="categorical",shrinkVar=opt$shrinkVar)
+write.csv(dmp,file="dmps.csv",quote=FALSE,row.names=TRUE)
+if(verbose){
+    cat("\n","DMP Finder successful \n")
+}
+
+
+# Model Matrix to pass into the bumphunter function
+T1= levels(pd$status)[2]
+T2= levels(pd$status)[1]
+
+# Introduce error if levels are different
+stopifnot(T1!=T2)
+
+keep=pd$status%in%c(T1,T2)
+tt=factor(pd$status[keep],c(T1,T2))
+design=model.matrix(~tt)
+
+if(verbose){
+    cat("Model matrix is: \n")
+    design
+}
+
+# Start bumphunter in a parallel environment
+# Parallelize over cores on machine
+registerDoParallel(cores = opt$cores)
+
+## Bumphunter Run with normalized_RGset processed with Quantile Normalization
+if (is(normalized_RGset,"GenomicRatioSet")) {
+    res=bumphunter(normalized_RGset[,keep],design,B=opt$b_permutations,smooth=opt$smooth,cutoff= opt$cutoff,type="Beta")
+    bumps= res$tab
+} else if(is(normalized_RGset,"MethylSet")) {
+    # convert MethylSet (norm.swan) into GenomicRatioSet through Ratioset
+    normalized_RGset = ratioConvert(normalized_RGset, what = "both", keepCN = TRUE)
+    normalized_RGset = mapToGenome(normalized_RGset)
+    res = bumphunter(normalized_RGset[,keep],design=design,B=opt$b_permutations,smooth=opt$smooth,cutoff=opt$cutoff)
+    bumps = res$tab
+} else {
+    # This else case is never supposed to run,
+    # it will run only if the normalized_RGset object
+    # did not have the expected class type
+    cat("Bumphunter did not run properly","\n")
+    stopifnot(!is.null(bumps))
+}
+
+if(verbose){
+    cat("Bumphunter result", "\n")
+    head(bumps)
+}
+
+
+## Choose DMR's of a certain length threshold.
+## This helps reduce the size of DMRs early, and match
+## with genes closest to region
+bumps = bumps[bumps$L > opt$l_value,]
+genes <- annotateTranscripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
+tab=matchGenes(bumps,genes)
+annotated_dmrs=cbind(bumps,tab)
+
+if(verbose){
+    cat("Match with annotation\n")
+    head(annotated_dmrs)
+}
+
+# Save result, which contains DMR's and closest genes
+write.csv(annotated_dmrs,file = "dmrs.csv",quote=FALSE,row.names=FALSE)
+
+# Garbage collect
+gc()
+
+
+## TODO: FIX BLOCK FINDER
+# Block finder
+#library(sva)
+#pheno <- pData(GRset)
+#mod <- model.matrix(~as.factor(status), data=pheno)
+#mod0 <- model.matrix(~1, data=pheno)
+#sva.results <- sva(mval, mod, mod0)

diff -r 000000000000 -r 84361ce36a11 minfi_pipeline.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/minfi_pipeline.xml Tue Apr 19 11:10:25 2016 -0400

[

b'@@ -0,0 +1,223 @@\n+<?xml version="1.0" encoding="UTF-8"?>\n+<tool id="minfi_pipeline" name="Minfi pipeline" version="1.0">\n+ <description>to Analayze Illumina 450k data</description>\n+ <macros>\n+ <import>minfi_macros.xml</import>\n+ </macros>\n+ <expand macro="requirements" />\n+ <command detect_errors="exit_code"><![CDATA[mkdir minfi_temp\n+ &&\n+ cp "${minfi_get_files}" ./minfi_temp/minfi_config.txt\n+ &&\n+ echo \\$GALAXY_SLOTS\n+ &&\n+ Rscript ${__tool_directory__}/minfi_pipeline.R\n+ --quiet="TRUE"\n+ --preprocess="${preprocess.preprocess_method}"\n+ --cores="\\${GALAXY_SLOTS:-4}"\n+ #if str( $minfi_param_type.minfi_param_type_selector ) == "advanced":\n+ --numPositions=${minfi_param_type.numPositions}\n+ --shrinkVar=${minfi_param_type.shrinkVar}\n+ --b_permutations=${minfi_param_type.b_permutations}\n+ --smooth=${minfi_param_type.smooth}\n+ --cutoff=${minfi_param_type.cutoff}\n+ --l_value=${minfi_param_type.l_value}\n+ #else:\n+ --numPositions=1000\n+ --shrinkVar=TRUE\n+ --b_permutations=25\n+ --smooth=FALSE\n+ --cutoff=0.3\n+ --l_value=4\n+ #end if]]></command>\n+ <configfiles>\n+ <configfile name="minfi_get_files"><![CDATA[### Parse the HDA\'s to get the path of each forward and reverse dataset\n+ #for $key in $control.keys()\n+ control $control[$key].forward $control[$key].reverse $control[$key].name\n+ #end for\n+ #for $key in $case.keys()\n+ case $case[$key].forward $case[$key].reverse $case[$key].name\n+ #end for]]></configfile>\n+ </configfiles>\n+ <inputs>\n+ \n+ <param name="control" type="data_collection" format="idat" label="Condition 1/ Treatment" collection_type="list:paired" help="Input data needs to be a list of dataset pairs, where the files are in IDAT format" />\n+ <param name="case" type="data_collection" format="idat" label="Condition 2/ Wildtype" collection_type="list:paired" help="Input data needs to be a list of dataset pairs, where the files are in IDAT format" />\n+ <conditional name="preprocess">\n+ <param name="preprocess_method" type="select" label="Select Preprocessing Method">\n+ <option value="quantile">Quantile Normalization (Recommended)</option>\n+ <option value="funnorm">Functional Normalization (Recommended)</option>\n+ <option value="illumina">Illumina:Genome Studio Normalization</option>\n+ <option value="swan">Subset-quantile Within Array Normalisation</option>\n+ <option value="noob">Noob background correction method or Noob Normalization</option>\n+ </param>\n+ <when value="quantile" />\n+ <when value="funnorm" />\n+ <when value="illumina" />\n+ <when value="swan" />\n+ <when value="noob" />\n+ </conditional>\n+ <conditional name="minfi_param_type">\n+ <param name="minfi_param_type_selector" type="select" label="Basic or Advanced Minfi Parameters">\n+ <option value="basic" selected="True">Basic Default settings</option>\n+ <option value="advanced">Advanced</option>\n+ </param>\n+ <when value="basic">\n+ \n+ </when>\n+ <when value="advanced">\n+ \n+ <param name="numPositions" type="integer" value="1000" label="numPositions" help="Refer the tool\'s help section" />\n+ \n+ \n+ <par'..b're of one phenotype (Example: Cancer, Disease state, Phenotype 1)\n+\n+*Control* : Dataset collection with all samples which are of base normal phenotype (Example: Normals, Non-Disease state, Phenotype 2)\n+\n+*Select Preprocessing Method*:\n+\n+Choose one of the many preprocessing methods available. For more information on the different preprocessing methods refer to the minfi manual_, https://www.bioconductor.org/packages/release/bioc/manuals/minfi/man/minfi.pdf\n+\n+*NOTE*\n+Many people ask us which normalization they should apply to their dataset. A good rule recommended by the authors of the package is, If there exist global biological methylation differences between your samples, as for instance a dataset with cancer and normal samples, or a dataset with different tissues/cell types, use the preprocessFunnorm function as it is aimed for such datasets. On the other hand, if you do not expect global differences between your samples, for instance a blood dataset, or one-tissue dataset, use the preprocessQuantile function. In our experience, these two normalization procedures perform always better than the functions preprocessNoob, preprocessIllumina and preprocessSWAN discussed below. For convenience, these functions are still implemented in the minfi package. This section is taken from the excellent guide_ provided by Jean-Philippe Fortin and Kasper Daniel Hansen.\n+\n+\n+**OUTPUTS**:\n+\n+Plots:\n+\n+Output 1: PDF file of the QC Report.\n+Output 2: PDF file of the MDS plot.\n+\n+CSV files:\n+\n+Output 1: CSV file containing Differentially Methylated Positions.\n+Output 2: CSV file containing Differentially Methylated Regions calculated using Bumphunter.\n+Output 3: CSV file containing Large scale Differentially methylated regions.\n+\n+\n+**HOW TO USE**\n+\n+IDAT files (Both Red and Green channel). Make paired dataset collections, with RED and GREEN channel IDAT files.\n+\n+Step 1: Upload IDAT(Both Red and green channel) files using the upload tool in Galaxy.\n+\n+Step 2: Once the upload is completed, select the "Operations on Multiple Datasets" in the history panel.\n+\n+Step 3: Select the list of IDAT files to be analyzed, and click "For all selected".\n+\n+Step 4:\n+\n+Choose the "Build List of Dataset pairs". Make the pairs and label the dataset collections. Once you enter the "Create a collection of paried datasets" dialogue box, click on "Clear filters" and then choose the "Forward" == Green channel, and "Reverse" == Red channel files. You should see the pairs in green color in the bottom panel.\n+\n+Rename your common prefix for the file, by removing the trailing underscore "_", and name your collection. You should have one dataset collection for "Case" and another with "Control" (Normal vs Cancer or Treatment vs Wildtype)\n+\n+Step 5: Once the two dataset collections are prepared, run the tool to run a minfi pipeline.\n+\n+\n+**ADVANCED PARAMETERS:**\n+\n+Variance shrinkage (\xe2\x80\x98shrinkVar=TRUE\xe2\x80\x99) is recommended when sample sizes are small (<10).\n+The sample variances are squeezed by computing empirical Bayes posterior means using\n+the \xe2\x80\x98limma\xe2\x80\x99 package.\n+\n+\n+B: An integer denoting the number of resamples to use when computing null distributions.\n+This defaults to 0. If \xe2\x80\x98permutations\xe2\x80\x99 is supplied that defines the number of\n+permutations/bootstraps and \xe2\x80\x98B\xe2\x80\x99 is ignored.\n+\n+\n+smooth: A logical value. If TRUE the estimated profile will be smoothed with the smoother\n+defined by \xe2\x80\x98smoothFunction\xe2\x80\x99\n+\n+\n+cutoff: A numeric value. Values of the estimate of the genomic profile above the cutoff\n+or below the negative of the cutoff will be used as candidate regions. It is possible\n+to give two separate values (upper and lower bounds). If one value is given, the lower\n+bound is minus the value.\n+\n+.. _manual: https://www.bioconductor.org/packages/release/bioc/manuals/minfi/man/minfi.pdf\n+.. _guide: https://www.bioconductor.org/help/course-materials/2015/BioC2015/methylation450k.html]]></help>\n+ <expand macro="citations" />\n+</tool>\n\\ No newline at end of file\n'

diff -r 000000000000 -r 84361ce36a11 test-data/.Rhistory
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/.Rhistory Tue Apr 19 11:10:25 2016 -0400

@@ -0,0 +1,2 @@
+ls()
+setwd("~/Documents/galaxyproject/tools-iuc/tool_collections/methylation_toolkit/minfi/test-data/")

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+++ b/test-data/dmps.csv Tue Apr 19 11:10:25 2016 -0400

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diff -r 000000000000 -r 84361ce36a11 test-data/dmrs.csv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/dmrs.csv Tue Apr 19 11:10:25 2016 -0400

b"@@ -0,0 +1,27 @@\n+chr,start,end,value,area,cluster,indexStart,indexEnd,L,clusterL,p.value,fwer,p.valueArea,fwerArea,name,annotation,description,region,distance,subregion,insideDistance,exonnumber,nexons,UTR,strand,geneL,codingL,Entrez,subjectHits\n+chr6,29894050,29894248,-0.443684736764384,3.54947789411507,163250,159635,159642,8,48,0.000500380724464266,0.2,0.00184923311215055,0.56,HLA-J,NR_024240,inside intron,inside,36945,inside intron,-35825,3,5,inside transcription region,+,120628,NA,3137,29448\n+chr19,58739986,58740549,-0.441152378136395,3.08806664695476,102691,450330,450336,7,14,0.000674426193843142,0.2,0.0030022843467856,0.8,ZNF544,NM_014480 NP_055295 XM_011526738 XM_011526739 XM_011526740 XM_011526741 XM_011526742 XM_011526743 XM_011526744 XM_011526745 XM_011526746 XM_011526747 XM_011526748 XM_011526749 XM_011526750 XP_011525040 XP_011525041 XP_011525042 XP_011525043 XP_011525044 XP_011525045 XP_011525046 XP_011525047 XP_011525048 XP_011525049 XP_011525050 XP_011525051 XP_011525052,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,7,5' UTR,+,34667,NA,27300,25448\n+chr12,133464594,133464737,-0.362270811412091,4.34724973694509,52682,326621,326632,12,35,0.00034809093875775,0.28,0.000478625040791907,0.28,CHFR,NM_001161344 NM_001161345 NM_001161346 NM_001161347 NM_018223 NP_001154816 NP_001154817 NP_001154818 NP_001154819 NP_060693,promoter,promoter,390,NA,NA,NA,18,NA,-,47266,NA,55743,45788\n+chrX,48858679,48858799,-0.401188335652595,2.40713001391557,201885,477300,477305,6,11,0.00387251169367997,0.36,0.00789731317306646,0.84,GRIPAP1,NM_020137 NM_207672 NP_064522 XM_011543935 XM_011543936 XM_011543937 XP_011542237 XP_011542238 XP_011542239,promoter,promoter,4,NA,NA,NA,26,NA,-,28541,NA,56850,47248\n+chrX,147582041,147582727,-0.38052600311616,3.04420802492928,204133,483651,483658,8,16,0.00234961383661482,0.52,0.00317632981616447,0.8,AFF2,NM_001169122 NM_001169123 NM_001169124 NM_001169125 NM_001170628 NM_002025 NP_001162593 NP_001162594 NP_001162595 NP_001162596 NP_001164099 NP_002016,overlaps 5',overlaps 5',0,covers exon(s),0,1,20,overlaps 5' UTR,+,500054,NA,2334,20608\n+chr6,29894322,29894737,-0.375873438773213,2.63111407141249,163250,159645,159651,7,48,0.00328510823452627,0.64,0.00561296638746873,0.8,HLA-J,NR_024240,inside intron,inside,37217,inside intron,-36097,3,5,inside transcription region,+,120628,NA,3137,29448\n+chrX,134478016,134478627,-0.343779201221938,2.75023360977551,203855,482902,482909,8,16,0.00382900032633526,0.8,0.00480800609159143,0.8,ZNF449,NM_152695 NP_689908 XM_011531312 XM_011531313 XM_011531314 XP_011529614 XP_011529615 XP_011529616,overlaps two exons,inside,388,overlaps two exons,0,1,5,5' UTR,+,5611,NA,203523,16963\n+chrX,109561508,109561793,-0.368398978715782,2.57879285101048,203255,481182,481188,7,13,0.00406831284673121,0.8,0.00591754595888176,0.8,AMMECR1,NM_001025580 NM_001171689 NM_015365 NP_001020751 NP_001165160 NP_056180,inside intron,inside,121668,inside intron,-430,3,8,5' UTR,-,246047,NA,9949,73257\n+chrX,66764000,66764893,-0.363158425397901,2.1789505523874,202342,478567,478572,6,28,0.00720113129555096,0.84,0.0101163929076471,0.84,AR,NM_000044 NM_001011645 NP_000035 NP_001011645,inside exon,inside,126,inside exon,0,1,5,5' UTR,+,186587,NA,367,31668\n+chrX,21857598,21857776,-0.351499187340973,2.10899512404584,201131,475189,475194,6,11,0.00907212009137387,0.84,0.0112041770912651,0.84,MBTPS2,NM_015884 NP_056968,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,11,5' UTR,+,45885,NA,51360,39889\n+chrX,131156970,131157336,-0.342417150500017,2.0545029030001,203742,482548,482553,6,14,0.00989883607092353,0.84,0.0125095181116067,0.84,STK26,NM_001042452 NM_001042453 NM_016542 NP_001035917 NP_001035918 NP_057626 XM_011531349 XM_011531350 XP_011529651 XP_011529652,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,13,5' UTR,+,52726,NA,51765,40875\n+chrX,68835678,68835895,-0.338357354083116,2.0301441244987,202426,478794,478799,6,16,0.0103339497443707,0.84,0.0133362340911563,0.92,EDA,NM_001005609 NM_001005610 NM"..b"exon upstream,0,1,10,5' UTR,-,19002,NA,65109,54035\n+chrX,56589792,56589951,-0.37623840531352,1.8811920265676,202232,478280,478284,5,13,0.00981181333623409,0.92,0.0161209616012183,0.92,UBQLN2,NM_013444 NP_038472,promoter,promoter,75,NA,NA,NA,1,NA,+,3417,NA,29978,28433\n+chrX,153744609,153744915,-0.374058144562376,1.87029072281188,204611,484909,484913,5,14,0.0101816599586642,0.92,0.0163602741216143,0.92,FAM3A,NM_001171132 NM_001171133 NM_001171134 NM_001282311 NM_001282312 NM_021806 NP_001164603 NP_001164604 NP_001164605 NP_001269240 NP_001269241 NP_068578 XM_005274714 XM_005274716 XM_005277879 XM_006724831 XM_006724832 XM_006724833 XM_006724834 XM_011531185 XP_005274771 XP_005274773 XP_005277936 XP_006724894 XP_006724895 XP_006724896 XP_006724897 XP_011529487,promoter,promoter,43,NA,NA,NA,9,NA,-,11239,NA,60343,50709\n+chrX,108976811,108976893,-0.367057681471229,1.83528840735615,203243,481151,481155,5,11,0.0115305123463505,0.92,0.0170782116828021,0.92,ACSL4,NM_004458 NM_022977 NP_004449 NP_075266 XM_005262108 XM_005262109 XM_005262110 XM_006724635 XM_011530888 XM_011530889 XP_005262165 XP_005262166 XP_005262167 XP_006724698 XP_011529190 XP_011529191,promoter,promoter,190,NA,NA,NA,17,NA,-,92057,NA,2182,17552\n+chrX,49126128,49126241,-0.363642711949825,1.81821355974912,201952,477522,477526,5,11,0.0122049385401936,0.92,0.0176003480909388,0.92,PPP1R3F,NM_001184745 NM_033215 NP_001171674 NP_149992 XM_005272687 XP_005272744,promoter,promoter,65,NA,NA,NA,4,NA,+,18249,NA,89801,68492\n+chrX,118533106,118533179,-0.362334138509756,1.81167069254878,203439,481671,481675,5,9,0.0125747851626237,0.92,0.0179266833460241,0.92,SLC25A43,NM_145305 NP_660348 XR_938545 XR_938546 XR_938547,promoter,promoter,79,NA,NA,NA,5,NA,+,52848,NA,203427,16942\n+chrX,154444538,154444619,-0.359206062369577,1.79603031184788,204656,485062,485066,5,10,0.0140541716523442,0.92,0.0185358424888502,0.92,VBP1,NM_001303543 NM_001303544 NM_001303545 NM_003372 NP_001290472 NP_001290473 NP_001290474 NP_003363,promoter,promoter,82,NA,NA,NA,6,NA,+,23397,22363,7411,58421\n+chrX,119603098,119603333,-0.352906222085535,1.76453111042768,203532,481962,481966,5,7,0.0148373762645491,0.92,0.0193843141520722,0.92,LAMP2,NM_001122606 NM_002294 NM_013995 NP_001116078 NP_002285 NP_054701,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,9,5' UTR,-,32855,30015,3920,33457\n+chrX,74493807,74494066,-0.347560039708178,1.73780019854089,202688,479532,479536,5,8,0.016099205917546,0.92,0.0201457630806048,0.92,UPRT,NM_001307944 NM_145052 NP_001294873 NP_659489 NR_030774 XM_011530866 XM_011530867 XP_011529168 XP_011529169,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,7,5' UTR,+,30838,26733,139596,11345\n+chrX,100878362,100879169,-0.338224986470994,1.69112493235497,202950,480298,480302,5,15,0.0182095072337648,0.92,0.0219514848254106,0.92,ARMCX3,NM_016607 NM_177947 NM_177948 NP_057691 NP_808816 NP_808817 XM_005262141 XP_005262198,covers exon(s),inside,11,covers exon(s),0,1,5,5' UTR,+,4480,1139,51566,40406\n+chrX,100603678,100603782,-0.335617480620908,1.67808740310454,202924,480191,480195,5,18,0.0185358424888502,0.92,0.0221690416621342,0.92,TIMM8A,NM_001145951 NM_004085 NM_032696 NP_001139423 NP_004076,inside exon,inside,175,inside exon,0,1,2,5' UTR,-,931,277,1678,14879\n+chrX,135067468,135067793,-0.331028693806678,1.65514346903339,203888,482996,483000,5,12,0.019493092570434,0.92,0.0229304905906668,0.92,SLC9A6,NM_001042537 NM_001177651 NM_006359 NP_001036002 NP_001171122 NP_006350 XM_006724726 XM_011531243 XP_006724789 XP_011529545,overlaps 5',overlaps 5',0,overlaps exon upstream,0,1,16,overlaps 5' UTR,+,61842,59221,10479,4965\n+chrX,19002577,19003031,-0.328309305179836,1.64154652589918,201059,474995,474999,5,12,0.0197106494071576,0.92,0.0234743826824758,0.92,PHKA2,NM_000292 NP_000283 XM_005274548 XM_005274550 XM_006724496 XM_006724498 XM_011545537 XM_011545538 XP_005274605 XP_005274607 XP_006724559 XP_006724561 XP_011543839 XP_011543840 XR_950461,promoter,promoter,97,NA,NA,NA,33,NA,-,92064,90447,5256,41117\n"

diff -r 000000000000 -r 84361ce36a11 test-data/mds_plot.pdf

Binary file test-data/mds_plot.pdf has changed

diff -r 000000000000 -r 84361ce36a11 test-data/qc_report.pdf

Binary file test-data/qc_report.pdf has changed

diff -r 000000000000 -r 84361ce36a11 tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml Tue Apr 19 11:10:25 2016 -0400

@@ -0,0 +1,10 @@
+<?xml version="1.0"?>
+<tool_dependency>
+    <package name="minfi" version="1.16.0">
+     # TODO:
+        <repository changeset_revision="f453b1c037bc" name="package_minfi_1_16_0" owner="nturaga" toolshed="https://toolshed.g2.bx.psu.edu" />
+    </package>
+    <package name="R" version="3.2.1">
+        <repository changeset_revision="d0bf97420fb5" name="package_r_3_2_1" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu" />
+    </package>
+</tool_dependency>