| Next changeset 1:b1fa45bd2b44 (2017-05-02) |
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Commit message:
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added:
GalFilter/README.txt GalFilter/RcheckLibrary.R GalFilter/filter_script.R GalFilter/filter_wrap.R GalFilter/generic_filter.xml GalFilter/miniTools.R GalFilter/static/images/Thumbs.db GalFilter/static/images/filter.png |
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| diff -r 000000000000 -r 2c9afaf849ad GalFilter/README.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GalFilter/README.txt Thu Feb 23 04:39:36 2017 -0500 |
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| @@ -0,0 +1,36 @@ +## ****** global_filter environnemnt : ****** ## +# version December 2014 M Landi / M Petera + +## --- PERL compilator / libraries : --- ## +NA +-- + +## --- R bin and Packages : --- ## +$ R --version +R version 3.0.2 (2013-05-16) -- "Good Sport" +Platform: x86_64-redhat-linux-gnu (64-bit) + +The dependent libs are : +>install.packages("batch", dep=TRUE) +-- + +## --- Binary dependencies --- ## +NA +-- + +## --- Config : --- ## +NA +-- + +## --- XML HELP PART --- ## +one image : +filter.png +-- + +## --- DATASETS --- ## +No data set ! waiting for galaxy pages +-- + +## --- ??? COMMENTS ??? --- ## +!WARNING! : Its existence made obsolete the old "Filters" tool +-- \ No newline at end of file |
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| diff -r 000000000000 -r 2c9afaf849ad GalFilter/RcheckLibrary.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GalFilter/RcheckLibrary.R Thu Feb 23 04:39:36 2017 -0500 |
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| @@ -0,0 +1,124 @@ +###################################################### +# R check library +# Coded by: M.Petera, +# - - +# R functions to use in R scripts +# (management of various generic subroutines) +# - - +# V0: script structure + first functions +# V1: More detailed error messages in match functions +###################################################### + + +# Generic function to return an error if problems have been encountered - - - - + +check.err <- function(err.stock){ + + # err.stock = vector of results returned by check functions + + if(length(err.stock)!=0){ stop("\n- - - - - - - - -\n",err.stock,"\n- - - - - - - - -\n") } + +} + + + + +# Table match check functions - - - - - - - - - - - - - - - - - - - - - - - - - + +# To check if dataMatrix and (variable or sample)Metadata match regarding identifiers +match2 <- function(dataMatrix, Metadata, Mtype){ + + # dataMatrix = data.frame containing dataMatrix + # Metadata = data.frame containing sampleMetadata or variableMetadata + # Mtype = "sample" or "variable" depending on Metadata content + + err.stock <- NULL # error vector + + id2 <- Metadata[,1] + if(Mtype=="sample"){ id1 <- colnames(dataMatrix)[-1] } + if(Mtype=="variable"){ id1 <- dataMatrix[,1] } + + if( length(which(id1%in%id2))!=length(id1) || length(which(id2%in%id1))!=length(id2) ){ + err.stock <- c("\nData matrix and ",Mtype," metadata do not match regarding ",Mtype," identifiers.") + if(length(which(id1%in%id2))!=length(id1)){ + if(length(which(!(id1%in%id2)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the data matrix\n", + " do not appear in the ",Mtype," metadata file:\n") + identif <- id1[which(!(id1%in%id2))][1:min(3,length(which(!(id1%in%id2))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + if(length(which(id2%in%id1))!=length(id2)){ + if(length(which(!(id2%in%id1)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the ",Mtype," metadata file\n", + " do not appear in the data matrix:\n") + identif <- id2[which(!(id2%in%id1))][1:min(3,length(which(!(id2%in%id1))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + err.stock <- c(err.stock,"\nPlease check your data.\n") + } + + return(err.stock) + +} + +# To check if the 3 standard tables match regarding identifiers +match3 <- function(dataMatrix, sampleMetadata, variableMetadata){ + + # dataMatrix = data.frame containing dataMatrix + # sampleMetadata = data.frame containing sampleMetadata + # variableMetadata = data.frame containing variableMetadata + + err.stock <- NULL # error vector + + id1 <- colnames(dataMatrix)[-1] + id2 <- sampleMetadata[,1] + id3 <- dataMatrix[,1] + id4 <- variableMetadata[,1] + + if( length(which(id1%in%id2))!=length(id1) || length(which(id2%in%id1))!=length(id2) ){ + err.stock <- c(err.stock,"\nData matrix and sample metadata do not match regarding sample identifiers.") + if(length(which(id1%in%id2))!=length(id1)){ + if(length(which(!(id1%in%id2)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the data matrix\n", + " do not appear in the sample metadata file:\n") + identif <- id1[which(!(id1%in%id2))][1:min(3,length(which(!(id1%in%id2))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + if(length(which(id2%in%id1))!=length(id2)){ + if(length(which(!(id2%in%id1)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the sample metadata file\n", + " do not appear in the data matrix:\n") + identif <- id2[which(!(id2%in%id1))][1:min(3,length(which(!(id2%in%id1))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + } + + if( length(which(id3%in%id4))!=length(id3) || length(which(id4%in%id3))!=length(id4) ){ + err.stock <- c(err.stock,"\nData matrix and variable metadata do not match regarding variable identifiers.") + if(length(which(id3%in%id4))!=length(id3)){ + if(length(which(!(id3%in%id4)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the data matrix\n", + " do not appear in the variable metadata file:\n") + identif <- id3[which(!(id3%in%id4))][1:min(3,length(which(!(id3%in%id4))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + if(length(which(id4%in%id3))!=length(id4)){ + if(length(which(!(id4%in%id3)))<4){ err.stock <- c(err.stock,"\n The ") + }else{ err.stock <- c(err.stock,"\n For example, the ") } + err.stock <- c(err.stock,"following identifiers found in the variable metadata file\n", + " do not appear in the data matrix:\n") + identif <- id4[which(!(id4%in%id3))][1:min(3,length(which(!(id4%in%id3))))] + err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") + } + } + + if(length(err.stock)!=0){ err.stock <- c(err.stock,"\nPlease check your data.\n") } + + return(err.stock) + +} \ No newline at end of file |
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| diff -r 000000000000 -r 2c9afaf849ad GalFilter/filter_script.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GalFilter/filter_script.R Thu Feb 23 04:39:36 2017 -0500 |
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| b'@@ -0,0 +1,226 @@\n+################################################################################################\r\n+# GENERIC FILTERS #\r\n+# #\r\n+# User: Galaxy #\r\n+# Starting date: 03-09-2014 #\r\n+# V-1.0: Restriction of old filter script to Filter according to factors #\r\n+# V-1.1: Choice of metadata table for filtering added ; data check added ; handling of NA ; #\r\n+# check for minimum remaining data #\r\n+# V-1.2: Minor modifications in script layout #\r\n+# V-2.0: Addition of numerical filter #\r\n+# V-2.1: Handling special characters #\r\n+# #\r\n+# #\r\n+# Input files: dataMatrix ; sampleMetadata ; variableMetadata #\r\n+# Output files: dataMatrix ; sampleMetadata ; variableMetadata #\r\n+# #\r\n+################################################################################################\r\n+\r\n+# Parameters (for dev)\r\n+if(FALSE){\r\n+ \r\n+ ion.file.in <- "test/ressources/inputs/ex_data_IONS.txt" #tab file\r\n+ meta.samp.file.in <- "test/ressources/inputs/ex_data_PROTOCOLE1.txt" #tab file\r\n+ meta.ion.file.in <- "test/ressources/inputs/ex_data_METAION.txt" #tab file\r\n+ \r\n+ ion.file.out <- "test/ressources/outputs/ex_data_IONS_fl.txt" #tab file\r\n+ meta.samp.file.out <- "test/ressources/outputs/ex_data_PROTOCOLE1_fl.txt" #tab file\r\n+ meta.ion.file.out <- "test/ressources/outputs/ex_data_METAION_fl.txt" #tab file\r\n+\r\n+NUM <- TRUE ; if(NUM){ls.num<-list(c("sample","injectionOrder","upper","20"),c("variable","var1","extremity","0.12","500"))}else{ls.num<-NULL}\r\n+ \r\n+FACT <- TRUE ; if(FACT){ls.fact<-list(c("centre","C","sample"),c("var2","A","variable"))}else{ls.fact<-NULL}\r\n+ \r\n+}\r\n+\r\n+filters <- function(ion.file.in, meta.samp.file.in, meta.ion.file.in,\r\n+ NUM, ls.num, FACT, ls.fact,\r\n+ ion.file.out, meta.samp.file.out, meta.ion.file.out){\r\n+ # This function allows to filter variables and samples according to factors or numerical values. \r\n+ # It needs 3 datasets: the data matrix, the variables\' metadata, the samples\' metadata. \r\n+ # It generates 3 new datasets corresponding to the 3 inputs filtered. \r\n+ #\r\n+ # Parameters:\r\n+ # - xxx.in: input files\' access\r\n+ # - xxx.out: output files\' access\r\n+ # - NUM: filter according to numerical variables yes/no\r\n+ # | > ls.num: numerical variables\' list for filter\r\n+ # - FACT: filter according to factors yes/no\r\n+ # | > ls.fact: factors\' list for filter\r\n+ \r\n+ \r\n+# Input -----------------------------------------------------------------------------------\r\n+\r\n+ion.data <- read.table(ion.file.in,sep="\\t",header=TRUE,check.names=FALSE)\r\n+meta.samp.data <- read.table(meta.samp.file.in,sep="\\t",header=TRUE,check.names=FALSE)\r\n+meta.ion.data <- read.table(meta.ion.file.in,sep="\\t",header=TRUE,check.names=FALSE)\r\n+\r\n+# Error vector\r\n+err.stock <- "\\n"\r\n+\r\n+\r\n+# Table match check \r\n+table.check <- match3(ion.data,meta.samp.data,meta.ion.data)\r\n+check.err(table.check)\r\n+\r\n+# StockID\r\n+samp.id <- stockID(ion.data,meta.samp.data,"sample")\r\n+ion.data <- samp.id$dataMatrix\r\n+meta.samp.data <- samp.id$Metadata\r\n+samp.id <- samp.id$id.match\r\n+\r\n+\r\n+\r\n+# Function 1:'..b'which(metadata[,numcol]>as.numeric(ls.num[[i]][5])))\r\n+ if(length(toremove)!=0){\r\n+ metadata <- metadata[-c(toremove),]\r\n+ }\r\n+ }}}}\r\n+ \r\n+ # Extension to the tables\r\n+ if(ls.num[[i]][1]=="sample"){\r\n+ meta.samp.data <- metadata\r\n+ ion.data <- ion.data[,c(1,which(colnames(ion.data)%in%meta.samp.data[,1]))]\r\n+ }else{\r\n+ meta.ion.data <- metadata\r\n+ ion.data <- ion.data[which(ion.data[,1]%in%meta.ion.data[,1]),]\r\n+ }\r\n+ \r\n+ }}}\r\n+ \r\n+} # end if(NUM)\r\n+# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - -\r\n+\r\n+\r\n+\r\n+# Function 2: Filter according to factors -------------------------------------------------\r\n+# Allows to delete all elements corresponding to selected value of designated factor.\r\n+if(FACT){\r\n+\r\n+ # For each factor to filter\r\n+ for(i in 1:length(ls.fact)){\r\n+ \r\n+\t# Which metadata table is concerned\r\n+\tif(ls.fact[[i]][3]=="sample"){metadata <- meta.samp.data}else{metadata <- meta.ion.data}\r\n+\t\r\n+ # Checking the columns and factors variables\r\n+ numcol <- which(colnames(metadata)==ls.fact[[i]][1])\r\n+ if(length(numcol)==0) {\r\n+ err.stock <- c(err.stock,"\\n-------",\r\n+ "\\nWarning: no \'",ls.fact[[i]][1],"\' column detected in ",ls.fact[[i]][3],\r\n+ " metadata!","\\nFiltering impossible for this factor.\\n-------\\n") \r\n+ }else{\r\n+ if((!(ls.fact[[i]][2]%in%levels(as.factor(metadata[,numcol]))))&((ls.fact[[i]][2]!="NA")|(length(which(is.na(metadata[,numcol])))==0))){\r\n+ err.stock <- c(err.stock,"\\n-------",\r\n+ "\\nWarning: no \'",ls.fact[[i]][2],"\' level detected in \'",\r\n+ ls.fact[[i]][1],"\' column (",ls.fact[[i]][3]," metadata)!\\n",\r\n+\t\t\t\t\t "Filtering impossible for this factor.\\n-------\\n")\r\n+ }else{\r\n+ \r\n+ # Filtering\r\n+ if(length(which(metadata[,numcol]==ls.fact[[i]][2]))!=0){ #if the level still exists in the data\r\n+ metadata <- metadata[-c(which(metadata[,numcol]==ls.fact[[i]][2])),]\r\n+\t}else{ #to treat the special case of "NA" level\r\n+\t if(ls.fact[[i]][2]=="NA"){metadata <- metadata[-c(which(is.na(metadata[,numcol]))),]}\r\n+\t}\r\n+\t\r\n+\t# Extension to the tables\r\n+\tif(ls.fact[[i]][3]=="sample"){\r\n+\t meta.samp.data <- metadata\r\n+ ion.data <- ion.data[,c(1,which(colnames(ion.data)%in%meta.samp.data[,1]))]\r\n+\t}else{\r\n+\t meta.ion.data <- metadata\r\n+ ion.data <- ion.data[which(ion.data[,1]%in%meta.ion.data[,1]),]\r\n+\t}\r\n+\r\n+ }}}\r\n+\r\n+} # end if(FACT)\r\n+# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - -\r\n+\r\n+\r\n+\r\n+\r\n+# Check if at least one sample and one variable remain ------------------------------------\r\n+\r\n+if(nrow(meta.samp.data)==0){\r\n+ stop("\\n /!\\\\ Your filtering options lead to no more sample in your data matrix!\\n",\r\n+ "Think about reducing your number of filter.")\r\n+}\r\n+\r\n+if(nrow(meta.ion.data)==0){\r\n+ stop("\\n /!\\\\ Your filtering options lead to no more variable in your data matrix!\\n",\r\n+ "Think about reducing your number of filter.")\r\n+}\r\n+\r\n+# Output ----------------------------------------------------------------------------------\r\n+\r\n+# Getting back original identifiers\r\n+id.ori <- reproduceID(ion.data,meta.samp.data,"sample",samp.id)\r\n+ion.data <- id.ori$dataMatrix\r\n+meta.samp.data <- id.ori$Metadata\r\n+\r\n+\r\n+# Error checking\r\n+if(length(err.stock)>1){\r\n+ stop(err.stock)\r\n+}else{\r\n+\r\n+write.table(ion.data, ion.file.out, sep="\\t", row.names=FALSE, quote=FALSE)\r\n+write.table(meta.samp.data, meta.samp.file.out, sep="\\t", row.names=FALSE, quote=FALSE)\r\n+write.table(meta.ion.data, meta.ion.file.out, sep="\\t", row.names=FALSE, quote=FALSE)\r\n+\r\n+}\r\n+\r\n+\r\n+} # end of filters function\r\n+\r\n+\r\n+# Typical function call\r\n+#filters(ion.file.in, meta.samp.file.in, meta.ion.file.in, \r\n+# NUM, ls.num, FACT, ls.fact,\r\n+# ion.file.out, meta.samp.file.out, meta.ion.file.out)\r\n+\r\n' |
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| diff -r 000000000000 -r 2c9afaf849ad GalFilter/filter_wrap.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GalFilter/filter_wrap.R Thu Feb 23 04:39:36 2017 -0500 |
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| @@ -0,0 +1,63 @@ +#!/usr/bin/Rscript --vanilla --slave --no-site-file + +################################################################################################ +# WRAPPER FOR filter_script.R (GENERIC FILTERS) # +# # +# Author: Melanie PETERA based on Marion LANDI's filters' wrapper # +# User: Galaxy # +# Original data: used with filter_script.R # +# Starting date: 04-09-2014 # +# V-1: Restriction of old filter wrapper to Filter according to factors # +# V-1.1: Modification to allow the choice of meta-data table for filtering # +# V-2: Addition of numerical filter # +# # +# # +# Input files: dataMatrix.txt ; sampleMetadata.txt ; variableMetadata.txt # +# Output files: dataMatrix.txt ; sampleMetadata.txt ; variableMetadata.txt # +# # +################################################################################################ + + +library(batch) #necessary for parseCommandArgs function +args = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects + +source_local <- function(...){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + for(i in 1:length(list(...))){source(paste(base_dir, list(...)[[i]], sep="/"))} +} +#Import the different functions +source_local("filter_script.R","RcheckLibrary.R","miniTools.R") + + +if(length(args) < 8){ stop("NOT enough argument !!!") } + +list_num <- NULL +if(!is.null(args$parm_col)){ + for( i in which(names(args)=="num_file") ){ + if(args[[i+2]] %in% c("lower","upper")){ + list_num <- c(list_num, list(c(args[[i]], args[[i+1]], args[[i+2]],args[[i+3]]))) + } + if(args[[i+2]] %in% c("between","extremity")){ + list_num <- c(list_num, list(c(args[[i]], args[[i+1]], args[[i+2]],args[[i+3]],args[[i+4]]))) + } + } +} + +list_fact <- NULL +if(!is.null(args$factor_col)){ + for( i in which(names(args)=="qual_file") ){ + list_fact <- c(list_fact, list(c(args[[i+1]], args[[i+2]], args[[i]]))) + } +} + +filters(args$dataMatrix_in, args$sampleMetadata_in, args$variableMetadata_in, + args$Numeric, list_num, args$Factors, list_fact, + args$dataMatrix_out, args$sampleMetadata_out, args$variableMetadata_out) + +#filters(ion.file.in, meta.samp.file.in, meta.ion.file.in, +# NUM, ls.num, FACT, ls.fact, +# ion.file.out, meta.samp.file.out, meta.ion.file.out) + +#delete the parameters to avoid the passage to the next tool in .RData image +rm(args) |
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| diff -r 000000000000 -r 2c9afaf849ad GalFilter/generic_filter.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GalFilter/generic_filter.xml Thu Feb 23 04:39:36 2017 -0500 |
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| b'@@ -0,0 +1,257 @@\n+<tool id="generic_filter" name="Generic_Filter" version="2016.03.03">\r\n+ <description>Removes elements according to numerical or qualitative values</description>\r\n+ <command interpreter="Rscript">\r\n+\tfilter_wrap.R\r\n+ dataMatrix_in "$dataMatrix_in"\r\n+ sampleMetadata_in "$sampleMetadata_in"\r\n+ variableMetadata_in "$variableMetadata_in"\r\n+ \r\n+\t Numeric "${numeric_condition.Factors}"\r\n+\t #if str($numeric_condition.Factors) == \'TRUE\':\r\n+\t\t#for $i in $numeric_condition.numeric_repeat:\r\n+\t\t num_file "${i.num_file}"\r\n+\t\t parm_col "${i.parm_col_num}"\r\n+\t\t Interval "${i.interval_condition.interval}"\r\n+\t\t #if str($i.interval_condition.interval) == \'lower\':\r\n+\t\t low_value "${i.interval_condition.low_value}"\r\n+\t\t #elif str($i.interval_condition.interval) == \'upper\':\r\n+\t\t up_value "${i.interval_condition.up_value}"\r\n+\t\t #elif str($i.interval_condition.interval) == \'between\':\r\n+\t\t low_value "${i.interval_condition.low_value}"\r\n+\t\t up_value "${i.interval_condition.up_value}"\r\n+\t\t #elif str($i.interval_condition.interval) == \'extremity\':\r\n+\t\t low_value "${i.interval_condition.low_value}"\r\n+\t\t up_value "${i.interval_condition.up_value}"\r\n+\t\t #end if\r\n+\t\t#end for\r\n+\t #end if\r\n+ \r\n+ Factors "${qualitative_condition.Factors}"\r\n+ #if str($qualitative_condition.Factors) == \'TRUE\':\r\n+ #for $i in $qualitative_condition.qualitative_repeat:\r\n+\t\t qual_file "${i.qual_file}"\r\n+\t\t factor_col "${i.factor_col}"\r\n+\t\t factors_value "${i.factors_value}"\r\n+ #end for\r\n+ #end if\r\n+ \r\n+ dataMatrix_out "$dataMatrix_out"\r\n+ sampleMetadata_out "$sampleMetadata_out"\r\n+ variableMetadata_out "$variableMetadata_out"\r\n+ </command>\r\n+ \r\n+ <inputs>\r\n+ <param name="dataMatrix_in" type="data" label="Data Matrix file" help="" format="tabular" />\r\n+ <param name="sampleMetadata_in" type="data" label="Sample metadata file" help="" format="tabular" />\r\n+ <param name="variableMetadata_in" type="data" label="Variable metadata file" help="" format="tabular" />\r\n+ \t\r\n+\t<conditional name="numeric_condition">\r\n+ <param name="Factors" type="select" label="Deleting samples and/or variables according to Numerical values" help="">\r\n+ <option value="FALSE">no</option>\r\n+ <option value="TRUE">yes</option>\r\n+ </param>\r\n+ <when value="TRUE">\r\n+ <repeat name="numeric_repeat" title="Identify the parameter to filter " min="1">\r\n+ <param name="num_file" label="On file" type="select" display="radio" help="">\r\n+ <option value="sample">Sample metadata</option>\r\n+ <option value="variable">Variable metadata</option>\r\n+ </param>\r\n+ <param name="parm_col_num" label="Name of the column to filter" type="text" value="" help="" />\r\n+\t\t\t<conditional name="interval_condition">\r\n+ <param name="interval" type="select" label="Interval of values to remove" help="">\r\n+ <option value="lower">lower</option>\r\n+ <option value="upper">upper</option>\r\n+ <option value="between">between</option>\r\n+ <option value="extremity">extremity</option>\r\n+ </param>\r\n+\t\t\t <when value="lower">\r\n+\t\t\t <param name="low_value" type="float" label="Remove all values lower than" optional="true" value="0" help=""/>\r\n+\t\t\t </when>\r\n+\t\t\t <when value="upper">\r\n+\t\t\t <param name="up_value" type="float" label="Remove all values upper than" optional="true" value="0" help=""/>\r\n+\t\t\t </when>\r\n+\t\t\t <when value="between">\r\n+\t\t\t <param name="low_value" type="float" label="Remove all values between" optional="true" value="0" help=""/>\r\n+\t\t\t <param name="up_value" type="float" label="And" optional="true" value="1" help=""/>\r\n+\t\t\t </when>\r\n+\t\t\t <when value="extremity">\r\n+\t\t\t <param name="low_value" type="float" label="Remove all values lower than" optional="true" value="0" help=""/>\r\n+\t\t\t <param name="up_value" type'..b': Variable metadata file | tabular |\r\n++----------------------------+------------+\r\n+\r\n+| \r\n+| Missing values in numerical columns of data must be coded NA if you want to use the Numerical filter on them. \r\n+\r\n+\r\n+----------\r\n+Parameters\r\n+----------\r\n+\r\n+Deleting samples and/or variables according to Numerical values:\r\n+\t| If \'yes\' (not default): execution deletes all samples or variables (according to selection) for which the designated \r\n+\t| numerical parameter ("Name of the column to remove" field) equals the selected range of values (depending on the "Interval of values to remove" chosen).\r\n+\t| To delete multiple independent intervals of value from the same parameter, it is necessary to add as many times as necessary \r\n+\t| the corresponding column via the button "Add new Identify the parameter to filter".\r\n+\r\n+| *On file (only if \'yes\')*\r\n+| Choice between sample and variable filtering (referring to the input file containing the factor).\r\n+|\r\n+\r\n+| *Name of the column to filter (only if \'yes\')*\r\n+| Column name of the numerical parameter from which we want to remove a range of values.\r\n+|\r\n+\r\n+| *Interval of values to remove (only if \'yes\')*\r\n+| Allows to choose which type of interval should be removed:\r\n+| - lower: removes all values below a specified value\r\n+| - upper: removes all values above a specified value\r\n+| - between: removes all values both greater than a specified value and lower than another (upper) one\r\n+| - extremity: removes all values that are either lower than a specified value or greater than another (upper) one\r\n+|\r\n+\r\n+| *\'Remove all values\' + interval chosen (only if \'yes\')*\r\n+| Numerical value(s) used as threshold, depending on the type of interval chosen above.\r\n+| Only one value for \'lower\' and \'upper\'; two values for \'between\' and \'extremity\'. \r\n+|\r\n+\r\n+| *Notes:* \r\n+| - these parameters can be used several times using the "Add new Identify the parameter to filter" button\r\n+| - Numerical filter does not remove NA values; if you want to suppress them, use the Qualitative filter below\r\n+|\r\n+\r\n+\r\n+Deleting samples and/or variables according to Qualitative values\r\n+\t| If \'yes\' (not default): execution deletes all data samples or variables (according to selection) for which the designated\r\n+\t| factor ("Name of the column to remove" field) equals the selected level ("Remove factor when" field).\r\n+\t| To delete multiple values of the same factor, it is necessary to add as many times as necessary the corresponding factor \r\n+\t| via the button "Add new Removing a level in factor".\r\n+\t\r\n+| *On file (only if \'yes\')*\r\n+| Choice between sample and variable filtering (referring to the input file containing the factor).\r\n+|\r\n+\r\n+| *Name of the column to filter (only if \'yes\')*\r\n+| Column name of the factor from which we want to remove a level.\r\n+|\r\n+\r\n+| *Remove factor when (only if \'yes\')*\r\n+| Value that you want to delete from the selected column.\r\n+| Each line with that value of designated factor will be deleted.\r\n+|\r\n+\r\n+| *Notes:* \r\n+| - these parameters can be used several times using the "Add new Removing a level in factor" button\r\n+| - this filter can be used to filter NA in numerical parameters, or to remove a specific numerical value\r\n+|\r\n+\r\n+\r\n+------------\r\n+Output files\r\n+------------\r\n+\r\n+\r\n+dataMatrix\r\n+\t| tabular output\r\n+\t| Identical to the input Data Matrix file with the sample columns and variable lines deleted according to specified filters\r\n+\t|\r\n+\r\n+sampleMetadata\r\n+\t| tabular output\r\n+\t| Identical to the input Sample metadata file with the sample lines deleted according to specified filters\r\n+\t|\r\n+\r\n+variableMetadata\r\n+\t| tabular output\r\n+\t| Identical to the input Variable metadata file with the variable lines deleted according to specified filters\r\n+\t|\r\n+\r\n+\r\n+---------------------------------------------------\r\n+\r\n+---------------\r\n+Working example\r\n+---------------\r\n+\r\n+\r\n+.. class:: warningmark\r\n+\r\n+Soon see the corresponding "Page"\r\n+\r\n+\t</help>\r\n+</tool>\n\\ No newline at end of file\n' |
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| @@ -0,0 +1,133 @@ +##################################################### +# Mini tools for Galaxy scripting +# Coded by: M.Petera, +# - - +# R functions to use in R scripts and wrappers +# to make things easier (lightening code, reducing verbose...) +# - - +# V0: script structure + first functions +# V1: addition of functions to handle special characters in identifiers +##################################################### + + +# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +# Function to call packages without printing all the verbose +# (only getting the essentials, like warning messages for example) + +shyLib <- function(...){ + for(i in 1:length(list(...))){ + suppressPackageStartupMessages(library(list(...)[[i]],character.only=TRUE)) + } +} + +#example: shyLib("xcms","pcaMethods") + + + +# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +# Fonction pour sourcer les scripts R requis +# /!\ ATTENTION : actuellement la fonction n'est pas chargee au lancement du script, +# il faut donc la copier-coller dans le wrapper R pour pouvoir l'utiliser. + +if(FALSE){ +source_local <- function(...){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + for(i in 1:length(list(...))){ + source(paste(base_dir, list(...)[[i]], sep="/")) + } +} +} + +#example: source_local("filter_script.R","RcheckLibrary.R") + + + +# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +# Functions to stock identifiers before applying make.names() and +# to reinject it into final matrices +# Note: it reproduces the original order of datasets' identifiers +# - - - +# stockID: stocks original identifiers and original order +# -> needs checked data regarding table match +# reproduceID: reinjects original identifiers and original order into final tables +# -> function to be used at the very end, when exporting tables + +stockID <- function(dataMatrix, Metadata, Mtype){ + # dataMatrix = data.frame containing dataMatrix + # Metadata = data.frame containing sampleMetadata or variableMetadata + # Mtype = "sample" or "variable" depending on Metadata content + cname <- colnames(dataMatrix)[1] + # dataMatrix temporary-stock + transfo - - - - + if(Mtype=="sample"){ + id.ori <- colnames(dataMatrix)[-1] + colnames(dataMatrix) <- make.names(colnames(dataMatrix)) + } + if(Mtype=="variable"){ + id.ori <- dataMatrix[,1] + dataMatrix[,1] <- make.names(dataMatrix[,1]) + } + # global stock - - - - - - - - - - - - - - - - + id.new <- data.frame(order.ori=c(1:length(Metadata[,1])),Metadata[,1], + id.new=make.names(Metadata[,1]),id.ori, + id.new.DM=make.names(id.ori),stringsAsFactors=FALSE) + colnames(id.new)[c(2,4)] <- c(colnames(Metadata)[1],cname) + # Metadata transfo + returning data - - - - - + Metadata[,1] <- make.names(Metadata[,1]) + return(list(id.match=id.new, dataMatrix=dataMatrix, Metadata=Metadata)) +} +#example: A<-stockID(myDM,mysM,"sample") ; myDM<-A$dataMatrix ; mysM<-A$Metadata ; A<-A$id.match + +reproduceID <- function(dataMatrix, Metadata, Mtype, id.match){ + # dataMatrix = data.frame containing dataMatrix + # Metadata = data.frame containing sampleMetadata or variableMetadata + # Mtype = "sample" or "variable" depending on Metadata content + # id.match = 'id.match' element produced by stockID + #Metadada - - - - - - - - - - - - - - + temp.table <- id.match[,c(1,2,3)] + ## Removing deleted rows + for(i in 1:(dim(id.match)[1])){ + if(!(temp.table[i,3]%in%Metadata[,1])){temp.table[i,1] <- 0} + } + if(length(which(temp.table[,1]==0))!=0){ + temp.table <- temp.table[-c(which(temp.table[,1]==0)),] + } + ## Restoring original identifiers and order + temp.table <- merge(x=temp.table,y=Metadata,by.x=3,by.y=1) + temp.table <- temp.table[order(temp.table$order.ori),] + Metadata <- temp.table[,-c(1,2)] + rownames(Metadata) <- NULL + #dataMatrix - - - - - - - - - - - - - + rownames(dataMatrix)<-dataMatrix[,1] + if(Mtype=="sample"){ + dataMatrix <- t(dataMatrix[,-1]) + } + temp.table <- id.match[,c(1,4,5)] + ## Removing deleted rows + for(i in 1:(dim(id.match)[1])){ + if(!(temp.table[i,3]%in%rownames(dataMatrix))){temp.table[i,1] <- 0} + } + if(length(which(temp.table[,1]==0))!=0){ + temp.table <- temp.table[-c(which(temp.table[,1]==0)),] + } + ## Restoring original identifiers and order + temp.table <- merge(x=temp.table,y=dataMatrix,by.x=3,by.y=0) + temp.table <- temp.table[order(temp.table$order.ori),] + if(Mtype=="variable"){ + dataMatrix <- temp.table[,-c(1,2,4)] + colnames(dataMatrix)[1] <- colnames(id.match)[4] + } else { + rownames(temp.table) <- temp.table[,3] + temp.table <- t(temp.table[,-c(1,2,3)]) + dataMatrix <- data.frame(rownames(temp.table),temp.table) + colnames(dataMatrix)[1] <- colnames(id.match)[4] + } + rownames(dataMatrix) <- NULL + # return datasets - - - - - - - - - - - + return(list(dataMatrix=dataMatrix, Metadata=Metadata)) +} +#example: B<-reproduceID(myDM,mysM,"sample",A) ; myDM<-B$dataMatrix ; mysM<-B$Metadata + + + +# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
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