# HG changeset patch
# User theo.collard
# Date 1493209796 14400
# Node ID fa62657e9b571f4beeab519587a3717ca139b0b5
# Parent  940701e8bc59635222587c4f48fd4c39c7c7dad0
Uploaded

diff -r 940701e8bc59 -r fa62657e9b57 ballgown.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ballgown.xml	Wed Apr 26 08:29:56 2017 -0400
@@ -0,0 +1,235 @@
+<tool id="ballgown" name="Ballgown" version="0.5.0" workflow_compatible="true">
+  <description>Flexible, isoform-level differential expression analysis</description>
+  <requirements>
+    <requirement type="package" version="2.2.0">bioconductor-ballgown</requirement>
+    <requirement type="package" version="0.5.0">r-dplyr</requirement>
+    <requirement type="package" version="1.3.2">r-optparse</requirement>
+
+  </requirements>
+  <command interpreter="Rscript" detect_errors="aggressive">
+		##------------------------------------------------------------------------------------
+		## This function reads the input file with the mapping between samples and files
+		## E.g. of result:
+		## mapping = {
+		##     "e2t.ctab"   : "sample1",
+		##     "other.ctab" : "sample2",
+		##     "i2t.ctab"   : "sample1",
+		##     "t_data.ctab": "sample1"
+		##      ...
+		## }
+		##------------------------------------------------------------------------------------
+		#def read_sample_mapping_file(sample_mapping_file):
+			#try
+				#set mapping = {}
+				#set file = open($sample_mapping_file.dataset.dataset.get_file_name(),'r')
+				#for $line in $file:
+					#set content= $line.strip().split('\t')
+					#for $map in $content:
+						#set mapping[$map]= $content[0]
+					#end for
+				#end for
+				#return $mapping
+			#except
+				#return None
+			#end try
+		#end def
+
+		##------------------------------------------------------------------------------------
+		## This function returns the name of the sample associated to a given file
+		##------------------------------------------------------------------------------------
+		#def get_sample_name($dataset, $sample_mapping):
+			##If the file with samples mapping was provided
+			#if $sample_mapping != None:
+				#return $sample_mapping.get($dataset.name, None)
+			##Otherwise with extract the sample name from the filename
+			#else:
+				#return str($dataset.element_identifier)
+			#end if
+		#end def
+
+		##------------------------------------------------------------------------------------
+		## This function reads a dataset or list of datasets and sets the corresponding value
+		## in the $result variable
+		## e.g. of result
+		##'sample1' : {
+		##         'e_data': '/export/galaxy-central/database/files/000/dataset_13.dat'
+		##         'i_data': '/export/galaxy-central/database/files/000/dataset_10.dat',
+		##         't_data': '/export/galaxy-central/database/files/000/dataset_12.dat',
+		##         'e2t': '/export/galaxy-central/database/files/000/dataset_9.dat',
+		##         'i2t': '/export/galaxy-central/database/files/000/dataset_11.dat'
+		##      },
+		##------------------------------------------------------------------------------------
+		#def read_input_files($param_name, $param_value, $result, $sample_mapping, $create_if_empty):
+			## If input is a data collection
+			#if isinstance($param_value, list):
+				## For each dataset
+				#for $dataset in $param_value:
+					## Get the sample name
+					#set sample_name = $get_sample_name($dataset, $sample_mapping)
+					## Check if sample is already registered
+					#if not($result.has_key($sample_name)):
+						#if ($create_if_empty == True):
+							#set result[$sample_name] = {}
+						#else:
+							#raise ValueError("Error in input. Please check that input contains all the required files for sample " + $sample_name)
+						#end if
+					#end if
+					## Register the file to the sample
+					#set result[$sample_name][$param_name] = str($dataset.dataset.dataset.get_file_name())
+				#end for
+			#else:
+				#if not($result.has_key("sample_1")):
+					#set result["sample_1"] = {}
+				#end if
+				#set result["sample_1"][$param_name] = str($param_name.dataset.dataset.get_file_name())
+			#end if
+			#return $result
+		#end def
+
+		##------------------------------------------------------------------------------------
+		## Main body of the tool
+		##------------------------------------------------------------------------------------
+		## Set the params for the next R script
+		#set result={}
+		#set sample_mapping=None
+
+		## If the samples mapping file was provided, parse the content
+		#if $samples_names != None and not(isinstance($samples_names, list) and (None in $samples_names)):
+			#set sample_mapping = $read_sample_mapping_file($samples_names)
+		#end if
+
+		## READ THE CONTENT FOR e_data AND STORE THE FILES
+		## INDEXED BY THEIR SAMPLE NAME
+		## e.g. 'HBR_Rep1' : {
+		##         'e_data': '/export/galaxy-central/database/files/000/dataset_13.dat'
+		##         'i_data': '/export/galaxy-central/database/files/000/dataset_10.dat',
+		##         't_data': '/export/galaxy-central/database/files/000/dataset_12.dat',
+		##         'e2t': '/export/galaxy-central/database/files/000/dataset_9.dat',
+		##         'i2t': '/export/galaxy-central/database/files/000/dataset_11.dat'
+		##      },
+		##      'HBR_Rep2' : {...}
+		#set $result = $read_input_files("e_data.ctab", $e_data, $result, $sample_mapping, True)
+		#set $result = $read_input_files("i_data.ctab", $i_data, $result, $sample_mapping, False)
+		#set $result = $read_input_files("t_data.ctab", $t_data, $result, $sample_mapping, False)
+		#set $result = $read_input_files("e2t.ctab", $e2t, $result, $sample_mapping, False)
+		#set $result = $read_input_files("i2t.ctab", $i2t, $result, $sample_mapping, False)
+
+		## For each input sample, create a directory and link the input files for ballgown
+		#import os
+		#set n_sample = 1
+		#for $key, $value in $result.iteritems():
+			#set dir_name = str($output.files_path) + "/" + $key + "/"
+			$os.makedirs($dir_name)
+			#for $file_name, $file_path in $value.iteritems():
+				$os.symlink($file_path, $dir_name + $file_name)
+			#end for
+			#set n_sample = $n_sample + 1
+		#end for
+
+		## Run the R script with the location of the linked files and the name for outpot file
+		ballgown.R --directory $output.files_path --outputtranscript $output --outputgenes $outputgn --texpression $trexpression --phendat $phendata --bgout $bgo
+	</command>
+  <inputs>
+    <param name="e_data" type="data" multiple="true" format="tabular" label="Exon-level expression measurements" help="One row per exon. See below for more details."/>
+		<param name="i_data" type="data" multiple="true" format="tabular" label="Intron- (i.e., junction-) level expression measurements" help="One row per intron. See below for more details."/>
+		<param name="t_data" type="data" multiple="true" format="tabular" label="Transcript-level expression measurements" help="One row per transcript. See below for more details."/>
+		<param name="e2t" type="data" multiple="true" format="tabular" label="Exons-transcripts mapping" help="Table with two columns, e_id and t_id, denoting which exons belong to which transcripts. See below for more details."/>
+		<param name="i2t" type="data" multiple="true" format="tabular" label="Introns-transcripts mapping" help="Table with two columns, i_id and t_id, denoting which introns belong to which transcripts. See below for more details."/>
+		<param name="samples_names" type="data" optional="true" multiple="false" format="tabular" label="File names for samples" help="Optional. Use in case that the names for the analysed samples cannot be extracted from the filenames."/>
+    <param argument="--phendat" name="phendata" type="data" format="csv" label="phenotype data" />
+    <param argument="--texpression" name="trexpression" type="float" value="0.5" label="minimal transcript expression to appear in the results"/>
+  </inputs>
+  <outputs>
+    <data name="bgo" format="rda" file="ballgown_object.rda" label="${tool.name} on ${on_string}: ballgown object (R data file)"/>
+    <data name="output" format="csv" file="output_transcript.csv" label="${tool.name} on ${on_string}: transcripts expression (tabular)"/>
+    <data name="outputgn" format="csv" file="output_genes.csv" label="${tool.name} on ${on_string}: genes expression (tabular)"/>
+  </outputs>
+  <tests>
+  </tests>
+  <help>
+
+=======================
+Ballgown
+=======================
+-----------------------
+**What it does**
+-----------------------
+
+Ballgown is a software package designed to facilitate flexible differential expression analysis of RNA-seq data.
+The Ballgown package provides functions to organize, visualize, and analyze the expression measurements for your transcriptome assembly.
+
+----
+
+-----------------------
+**How to use**
+-----------------------
+The input for this tools consists on 5 files for each sample in your experiment:
+
+- **e_data**: exon-level expression measurements. Tab file or collection of tab files. One row per exon. Columns are e_id (numeric exon id), chr, strand, start, end (genomic location of the exon), and the following expression measurements for each sample:
+          * rcount: reads overlapping the exon
+          * ucount: uniquely mapped reads overlapping the exon
+          * mrcount: multi-map-corrected number of reads overlapping the exon
+          * cov average per-base read coverage
+          * cov_sd: standard deviation of per-base read coverage
+          * mcov: multi-map-corrected average per-base read coverage
+          * mcov_sd: standard deviation of multi-map-corrected per-base coverage
+- **i_data**: intron- (i.e., junction-) level expression measurements. Tab file or collection of tab files. One row per intron. Columns are i_id (numeric intron id), chr, strand, start, end (genomic location of the intron), and the following expression measurements for each sample:
+          * rcount: number of reads supporting the intron
+          * ucount: number of uniquely mapped reads supporting the intron
+          * mrcount: multi-map-corrected number of reads supporting the intron
+- **t_data**: transcript-level expression measurements. Tab file or collection of tab files. One row per transcript. Columns are:
+          * t_id: numeric transcript id
+          * chr, strand, start, end: genomic location of the transcript
+          * t_name: Cufflinks-generated transcript id
+          * num_exons: number of exons comprising the transcript
+          * length: transcript length, including both exons and introns
+          * gene_id: gene the transcript belongs to
+          * gene_name: HUGO gene name for the transcript, if known
+          * cov: per-base coverage for the transcript (available for each sample)
+          * FPKM: Cufflinks-estimated FPKM for the transcript (available for each sample)
+- **e2t**: Tab file or collection of tab files. Table with two columns, e_id and t_id, denoting which exons belong to which transcripts. These ids match the ids in the e_data and t_data tables.
+- **i2t**: Tab file or collection of tab files. Table with two columns, i_id and t_id, denoting which introns belong to which transcripts. These ids match the ids in the i_data and t_data tables.
+- samples_names: (optional) Tab file. Table with five columns, one row per sample. Defines which files from the input belong to each sample in the experiment.
+
+.. class:: infomark
+
+'''TIP''' *Note* Here's an example of a good phenotype data file for your expirement.
+
++--------------+-------------------------+-------------------------+---+
+|ids           |experimental variable 1  |experimental variable 2  |...|
++==============+=========================+=========================+===+
+|sample 1      |value 1                  |value 2                  |...|
++--------------+-------------------------+-------------------------+---+
+|sample 2      |value 2                  |value 1                  |...|
++--------------+-------------------------+-------------------------+---+
+|sample 3      |value 1                  |value 2                  |...|
++--------------+-------------------------+-------------------------+---+
+|sample 4      |value 2                  |value 1                  |...|
++--------------+-------------------------+-------------------------+---+
+|...           |value 1                  |value 2                  |...|
++--------------+-------------------------+-------------------------+---+
+
+
+.. class:: infomark
+
+*Note* The minimal transcript expression is a number used to filter the transcripts that
+are less or not expressed in our samples when compared to the genome
+
+-----------------------
+**Outputs**
+-----------------------
+
+This tool has 3 outputs:
+
+- **transcripts expression** : this is a csv file containing all the transcripts that are expressed above the transcripts expression value
+- **genes expression** : this is a csv file containing all the genes that are expressed above the transcripts expression value
+- **Ballgown object** : this is the ballgown object created during the process. This file can be re-used later for further analysis in a R console.
+
+----
+
+**Authors**: Théo Collard [SLU Global Bioinformatics Centre], Rafael Hernández de Diego [SLU Global Bioinformatics Centre], and Tomas Klingström [SLU Global Bioinformatics Centre]
+
+Sources are available at https://github.com/CollardT/Ballgown-Wrapper
+
+  </help>
+</tool>