def get( dataset_id, history_id = None ):
"""
Given the history_id that is displayed to the user, this function will
download the file from the history and stores it under /import/
Return value is the path to the dataset stored under /import/
"""
conf = _get_conf()
gi = get_galaxy_connection()
hc = HistoryClient( gi )
dc = DatasetClient( gi )
file_path = '/import/%s' % dataset_id
history_id = history_id or _get_history_id()
# Cache the file requests. E.g. in the example of someone doing something
# silly like a get() for a Galaxy file in a for-loop, wouldn't want to
# re-download every time and add that overhead.
if not os.path.exists(file_path):
dataset_mapping = dict( [(dataset['hid'], dataset['id']) for dataset in hc.show_history( history_id, contents=True )] )
try:
hc.download_dataset( history_id, dataset_mapping[dataset_id], file_path, use_default_filename=False, to_ext=None )
except:
dc.download_dataset(dataset_mapping[dataset_id], file_path, use_default_filename=False)
return file_path
def get(dataset_id, history_id=None):
"""
Given the history_id that is displayed to the user, this function will
download the file from the history and stores it under /import/
Return value is the path to the dataset stored under /import/
"""
conf = _get_conf()
gi = get_galaxy_connection()
hc = HistoryClient(gi)
dc = DatasetClient(gi)
file_path = '/import/%s' % dataset_id
history_id = history_id or _get_history_id()
# Cache the file requests. E.g. in the example of someone doing something
# silly like a get() for a Galaxy file in a for-loop, wouldn't want to
# re-download every time and add that overhead.
if not os.path.exists(file_path):
dataset_mapping = dict([
(dataset['hid'], dataset['id'])
for dataset in hc.show_history(history_id, contents=True)
])
try:
hc.download_dataset(history_id,
dataset_mapping[dataset_id],
file_path,
use_default_filename=False,
to_ext=None)
except:
dc.download_dataset(dataset_mapping[dataset_id],
file_path,
use_default_filename=False)
return file_path
def get( dataset_id ):
"""
Given the history_id that is displayed to the user, this function will
download the file from the history and stores it under /import/
Return value is the path to the dataset stored under /import/
"""
conf = _get_conf()
gi = get_galaxy_connection()
hc = HistoryClient( gi )
dc = DatasetClient( gi )
file_path = '/import/%s' % dataset_id
dataset_mapping = dict( [(dataset['hid'], dataset['id']) for dataset in hc.show_history(conf['history_id'], contents=True)] )
try:
hc.download_dataset(conf['history_id'], dataset_mapping[dataset_id], file_path, use_default_filename=False, to_ext=None)
except:
dc.download_dataset(dataset_mapping[dataset_id], file_path, use_default_filename=False)
return file_path
def runWorkflow(argDictionary, comparisons,samples):
from bioblend.galaxy import GalaxyInstance
from bioblend.galaxy.histories import HistoryClient
from bioblend.galaxy.tools import ToolClient
from bioblend.galaxy.workflows import WorkflowClient
from bioblend.galaxy.libraries import LibraryClient
import tempfile
import time
api_key = ''
galaxy_host = ''
gi = GalaxyInstance(url=galaxy_host, key=api_key)
history_client = HistoryClient(gi)
tool_client = ToolClient(gi)
workflow_client = WorkflowClient(gi)
library_client = LibraryClient(gi)
history = history_client.create_history(argDictionary['accessionNumber'])
comparisonsTable = tool_client.upload_file(comparisons, history['id'], file_type='txt')
sampleTable = tool_client.upload_file(samples, history['id'], file_type='tabular')
if argDictionary['site'] == "ENA":
#fastqs available on ENA
tool_inputs = {
"accessionNumber":argDictionary["ENA"],"sampleTable":{'id': sampleTable['outputs'][0]['id'], 'src': 'hda'}
}
#run the tool to get the data from ENA
tool_client.run_tool(history['id'],'getRNASeqExpressionData', tool_inputs)
#we want to wait until we have all datasets
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
#sleep until all the fastq files are findable
time.sleep(120)
dirpath = tempfile.mkdtemp()
fileList = getDatasetsByApproxName("files.tabular", history,history_client)[0]
fileList = history_client.download_dataset(history["id"],fileList["id"],dirpath)
num_lines = sum(1 for line in open(fileList)) -1
datasets=list()
while len(datasets)!=num_lines:
time.sleep(10)
datasets = getDatasetsByApproxName("fastq",history,history_client )
else: #for SRA
if argDictionary['single'] == "TRUE":
with open(samples) as tsvfile:
reader = csv.DictReader(tsvfile, delimiter='\t')
for sample in reader:
print (sample)
fileNames=str.split(sample["File"],"|")
for fileName in fileNames:
tool_inputs = {
"input|input_select":"accession_number",
"outputformat":"fastqsanger.gz",
"input|accession":fileName
}
#run the tool to get the single data from SRA
tool_client.run_tool(history['id'],'toolshed.g2.bx.psu.edu/repos/iuc/sra_tools/fastq_dump/2.8.1.3', tool_inputs)
else:
with open(samples) as tsvfile:
reader = csv.DictReader(tsvfile, delimiter='\t')
for sample in reader:
tool_inputs = {
"accession_number":sample["File"]
}
#run the tool to get the paired data from SRA
tool_client.run_tool(history['id'],'toolshed.g2.bx.psu.edu/repos/mandorodriguez/fastqdump_paired/fastq_dump_paired/1.1.4', tool_inputs)
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
datasets = getDatasetsByApproxName("fastq",history,history_client )
#get the fastQC tool
for fastq in datasets:
try:
tool_inputs = {'input_file' : {'id': fastq['id'], 'src': 'hda'}}
tool_client.run_tool(history['id'],'toolshed.g2.bx.psu.edu/repos/devteam/fastqc/fastqc/0.69', tool_inputs)
except Exception:
pass
#wait till complete
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
#make dataset collections for quantification using the fastq files
collections=list()
with open(samples) as tsvfile:
reader = csv.DictReader(tsvfile, delimiter='\t')
for row in reader:
datasets=list()
fileNames=str.split(row["File"],"|")
for fileName in fileNames:
datasets= datasets + getDatasetsByApproxName(fileName,history,history_client )
#make list of datasets
collections.append(makeDataSetCollection(datasets,row["Sample"],history,history_client))
#get the correct kallisto index
species = argDictionary['species'].lower()
index = getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name=species +"IndexFile")
index = {'id': index, 'src': 'hda'}
#run kallisto for every dataset collection
for collection in collections:
#set up the tool_inputs
tool_inputs = {'index' : index,'inputs' : {'id': collection['id'], 'src': 'hdca'} ,"single":argDictionary["single"],"stranded":argDictionary["stranded"]}
#often encounter connection broken error - possible problem with Certus server?
#bypass by ignoring the exception
tool_client.run_tool(history['id'],'kallistoQuant', tool_inputs)
# we want to wait until we have all datasets
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
# Run multiqc on kallisto logs and fastqc files
datasets = getDatasetsByApproxName("RawData",history,history_client )
kallistoLogs = getDatasetsByApproxName(".log", history, history_client)
tool_inputs = {}
for i, dataset in enumerate(datasets+kallistoLogs):
if not dataset["deleted"]:
if dataset in datasets:
software = 'fastqc'
else:
software = 'kallisto'
params = {'id' : dataset['id'], 'src': 'hda', 'name': dataset['name']}
tool_inputs.update({'results_%s|software_cond|software' % i: software, 'results_%s|input_file' % i: params})
# #summarise with the multiQC tool
tool_client.run_tool(history['id'],'multiqc', tool_inputs)
multiQc = getDatasetsByApproxName("multiqc",history,history_client)[0]
#get all the abundance files to convert to gene level counts matrix
datasets = getDatasetsByApproxName(".abundance",history,history_client )
#make a dataset collection for to make a countsMatrix
collection = makeDataSetCollection(datasets,"abundances",history,history_client)
#set up the tool_inputs
tool_inputs = {'inputs' : {'id': collection['id'], 'src': 'hdca'} ,"species":argDictionary['species']}
#convert abundances to gene level counts matrix
tool_client.run_tool(history['id'],'KallistoAbundancestoGeneCountMatrix', tool_inputs)
# A diry hack, we want to wait until we have all datasets
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
txi = getDatasetsByApproxName("txi",history,history_client)
#set up the tool_inputs for PCA
tool_inputs = {'txiData' : {'id': txi[0]['id'], 'src': 'hda'} ,'sampleTable' : {'id': sampleTable['outputs'][0]['id'], 'src': 'hda'} ,"species":argDictionary['species'],'technicalReplicates':argDictionary['technicalReplicates'],'batchCorrect':argDictionary['batchCorrect']}
#run deseq2
tool_client.run_tool(history['id'],'PCARNASeq', tool_inputs)
pca = getDatasetsByApproxName("PCA",history,history_client)[0]
#set up the tool_inputs for DESeq2
tool_inputs = {'txiData' : {'id': txi[0]['id'], 'src': 'hda'} ,'sampleTable' : {'id': sampleTable['outputs'][0]['id'], 'src': 'hda'} ,
'comparisonsTable' : {'id': comparisonsTable['outputs'][0]['id'], 'src': 'hda'} ,"foldChangeOnly":argDictionary['foldChangeOnly'],"species":argDictionary['species'],'technicalReplicates':argDictionary['technicalReplicates'],'batchCorrect':argDictionary['batchCorrect']}
#run deseq2
tool_client.run_tool(history['id'],'DESeq2FoldChange', tool_inputs)
#run chrdir
tool_client.run_tool(history['id'],'characteristicDirectionRNASeq', tool_inputs)
#we want to wait until we have all datasets
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
#get the foldchange data, cut and run pathway workflow
dataset_id = getFoldChangeData(history, history_client)['id']
return_collection = [{'accessionNo':argDictionary['accessionNumber'], 'foldChange': getUrl(dataset_id), 'PCA': getUrl(pca["id"]),'chrDirTable': getUrl(getMostRecentDatasetByName('chrDirTable.tabular', history, history_client)['id'])}]
number_of_comparisons = -1
for line in open(comparisons):
if not line.isspace():
number_of_comparisons += 1
for comparison in range(0, int(number_of_comparisons)):
tool_inputs = {
'foldChangeTable' : {'id': dataset_id, 'src': 'hda'},
'comparisonNumber' : comparison + 1
}
tool_client.run_tool(history['id'], 'cutFoldChangeTable', tool_inputs)
while getNumberNotComplete(history['id'], history_client) > 0:
time.sleep(10)
if argDictionary['species'] in ["Rat","Cow","Horse","Pig","Zebrafish"]:
pathwayAnalysisWorkflow = workflow_client.show_workflow('c9468fdb6dc5c5f1')
params = dict()
for key in pathwayAnalysisWorkflow['steps'].keys():
params[key] = argDictionary
if argDictionary['species'] == "Rat":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="ratStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="ratGeneLengths")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="HOM_AllOrganism.rpt")
if argDictionary['species'] == "Cow":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="cowStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="cowGeneLengths")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="HOM_AllOrganism.rpt")
if argDictionary['species'] == "Horse":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="horseStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="horseGeneLengths")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="Homology.horse.txt")
if argDictionary['species'] == "Pig":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="pigStringNetwork.txt")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="pigGeneLengths.tabular")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="Homology.pig.txt")
if argDictionary['species'] == "Zebrafish":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="zebrafishStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="zebrafishGeneLengths")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="HOM_AllOrganism.rpt")
pathwayDatamap = {'3' : {'id': homology, 'src': 'hda'},'2' : {'id': network, 'src': 'hda'},'1' : {'id': geneLengths, 'src': 'hda'}}
diffExpDataCollection = getDatasetsByName('cutTable.tabular', history, history_client)
for index, diffExpData in enumerate(diffExpDataCollection):
numCompleted = getNumberComplete(history['id'], history_client) + 10
print(numCompleted)
pathwayDatamap["0"] = {'id': diffExpData['id'], 'src': 'hda'}
workflow_client.invoke_workflow(pathwayAnalysisWorkflow['id'],
inputs = pathwayDatamap,
history_id = history['id'],
params = params)
comparisonDict = getRowFromCsv(comparisons, index)
if 'Factor1' in comparisonDict.keys():
comparisonDict['Factor'] = comparisonDict['Factor1'] + "." + comparisonDict['Factor2']
return_dict = {'accessionNo':argDictionary['accessionNumber'],
'factor':comparisonDict['Factor'],
'comparisonNum':comparisonDict['Numerator'],
'comparisonDenom':comparisonDict['Denominator'],
'foldChange': getUrl(diffExpData['id']),
'interactome': pathwayDatamap['0']['id'],
'exonLength': pathwayDatamap['2']['id']}
while getNumberComplete(history['id'], history_client) < numCompleted:
time.sleep(10)
return_dict['moduleNodes'] = getUrl(getMostRecentDatasetByName('moduleNodes.text',
history, history_client)['id'])
return_dict['modulePlots'] = getUrl(getMostRecentDatasetByName('modulePlots.pdf',
history, history_client)['id'])
return_dict['slimEnrichPathways'] = getUrl(getMostRecentDatasetByName('slimEnrichmentPathways.tabular',
history, history_client)['id'])
return_dict['enrichedDrugsReverse'] = getUrl(getMostRecentDatasetByName('enrichedDrugsReverse.tabular',
history, history_client)['id'])
return_dict['enrichedDrugsMimic'] = getUrl(getMostRecentDatasetByName('enrichedDrugsMimic.tabular',
history, history_client)['id'])
return_dict['enrichedTerms'] = getUrl(getMostRecentDatasetByName('enrichedTerms.tabular',
history, history_client)['id'])
return_dict['enrichedTerms.reduced'] = getUrl(getMostRecentDatasetByName('enrichedTerms.reduced.tabular',
history, history_client)['id'])
return_dict['GO.MDS'] = getUrl(getMostRecentDatasetByName('GO.MDS.html',
history, history_client)['id'])
return_collection.append(return_dict)
# Hard code keys to define the order
keys = ['accessionNo','multiQC','factor','PCA','chrDirTable','comparisonNum','comparisonDenom','foldChange',
'interactome','exonLength','moduleNodes','modulePlots',
'slimEnrichPathways','secretedProteins','enrichedDrugsReverse','enrichedDrugsMimic','enrichedTerms','enrichedTerms.reduced','GO.MDS']
outFileName = 'output/' + argDictionary['accessionNumber'] + '-workflowOutput.tsv'
with open(outFileName, 'wb') as csvFile:
# Get headers from last dictionary in collection as first doesn't contain all keys
csvOutput = csv.DictWriter(csvFile, keys, delimiter = "\t")
csvOutput.writeheader()
csvOutput.writerows(return_collection)
#tool_client.upload_file(outFileName, history['id'], file_type='tsv')
return return_collection
else:
pathwayAnalysisWorkflow = workflow_client.show_workflow('e85a3be143d5905b')
params = dict()
for key in pathwayAnalysisWorkflow['steps'].keys():
params[key] = argDictionary
if argDictionary['species'] == "Mouse":
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="mouseStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="MouseGeneLengths.tab")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="Homology.mouse.txt")
secretedReference=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="uniprot-secreted-mouse.txt")
pathwayDatamap = {'4' : {'id': secretedReference, 'src': 'hda'},'3' : {'id': homology, 'src': 'hda'},'2' : {'id': network, 'src': 'hda'},'1' : {'id': geneLengths, 'src': 'hda'}}
else:
network=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="humanStringNetwork")
geneLengths=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="geneLengths")
homology=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="Homology.mouse.txt")
secretedReference=getLibraryToolDataID(history=history,history_client=history_client,library_client=library_client,name="uniprot-secreted-human.txt")
pathwayDatamap = {'4' : {'id': secretedReference, 'src': 'hda'},'3' : {'id': homology, 'src': 'hda'},'2' : {'id': network, 'src': 'hda'},'1' : {'id': geneLengths, 'src': 'hda'}}
diffExpDataCollection = getDatasetsByName('cutTable.tabular', history, history_client)
for index, diffExpData in enumerate(diffExpDataCollection):
numCompleted = getNumberComplete(history['id'], history_client) + 14
print(numCompleted)
pathwayDatamap["0"] = {'id': diffExpData['id'], 'src': 'hda'}
#pathwayDatamap['1'] = {'id': diffExpData['id'], 'src': 'hda'}
workflow_client.invoke_workflow(pathwayAnalysisWorkflow['id'],
inputs = pathwayDatamap,
history_id = history['id'],
params = params)
comparisonDict = getRowFromCsv(comparisons, index)
if 'Factor1' in comparisonDict.keys():
comparisonDict['Factor'] = comparisonDict['Factor1'] + "." + comparisonDict['Factor2']
return_dict = {'accessionNo':argDictionary['accessionNumber'],
'factor':comparisonDict['Factor'],
'comparisonNum':comparisonDict['Numerator'],
'comparisonDenom':comparisonDict['Denominator'],
'foldChange': getUrl(diffExpData['id']),
'interactome': pathwayDatamap['0']['id'],
'exonLength': pathwayDatamap['2']['id']}
while getNumberComplete(history['id'], history_client) < numCompleted:
time.sleep(10)
return_dict['moduleNodes'] = getUrl(getMostRecentDatasetByName('moduleNodes.text',
history, history_client)['id'])
return_dict['modulePlots'] = getUrl(getMostRecentDatasetByName('modulePlots.pdf',
history, history_client)['id'])
return_dict['pathways'] = getUrl(getMostRecentDatasetByName('pathways.tabular',
history, history_client)['id'])
return_dict['enrichPlot'] = getUrl(getMostRecentDatasetByName('enrichmentPlot.png',
history, history_client)['id'])
return_dict['enrichmentTable'] = getUrl(getMostRecentDatasetByName('TF_EnrichmentTable.tabular',
history, history_client)['id'])
return_dict['slimEnrichPathways'] = getUrl(getMostRecentDatasetByName('slimEnrichmentPathways.tabular',
history, history_client)['id'])
return_dict['secretedProteins'] = getUrl(getMostRecentDatasetByName('secretedProteins.tabular',
history, history_client)['id'])
return_dict['enrichedDrugsReverse'] = getUrl(getMostRecentDatasetByName('enrichedDrugsReverse.tabular',
history, history_client)['id'])
return_dict['enrichedDrugsMimic'] = getUrl(getMostRecentDatasetByName('enrichedDrugsMimic.tabular',
history, history_client)['id'])
return_dict['enrichedTerms'] = getUrl(getMostRecentDatasetByName('enrichedTerms.tabular',
history, history_client)['id'])
return_dict['enrichedTerms.reduced'] = getUrl(getMostRecentDatasetByName('enrichedTerms.reduced.tabular',
history, history_client)['id'])
return_dict['GO.MDS'] = getUrl(getMostRecentDatasetByName('GO.MDS.html',
history, history_client)['id'])
return_collection.append(return_dict)
# Hard code keys to define the order
keys = ['accessionNo','multiQC','factor','PCA','chrDirTable','comparisonNum','comparisonDenom','foldChange',
'interactome','exonLength','moduleNodes','modulePlots','pathways','enrichPlot', 'enrichmentTable',
'slimEnrichPathways','secretedProteins','enrichedDrugsReverse','enrichedDrugsMimic','enrichedTerms','enrichedTerms.reduced','GO.MDS']
outFileName = 'output/' + argDictionary['accessionNumber'] + '-workflowOutput.tsv'
with open(outFileName, 'wb') as csvFile:
# Get headers from last dictionary in collection as first doesn't contain all keys
csvOutput = csv.DictWriter(csvFile, keys, delimiter = "\t")
csvOutput.writeheader()
csvOutput.writerows(return_collection)
return return_collection