def OnStatExport(self, event):
if not self.project.has_key('exportFA'):
outDir="%s/Export" % (self.project['directory'])
createDir(outDir)
self.project['exportFA']= ReportFA(outDir=outDir,name=self.project['name'], organism=self.project['organism'])
try:
self.project['exportFA'].saveStatistics(self.project['allFA'] , [statistics for statistics in self.workflow if self.viewStat])
self.popup.msg(self.frame, "Congratulations, statistics have been successfully exported to %s" % self.project['exportFA'].outDir, "Operation")
except :
self.popup.error(self.frame, "failed to export statistics to %s" % self.project['exportFA'].outDir)
def compareRandomizePipelines(projectDir):
"""
This function compare the properties of 3 randomized functional annotations for a Bovine array.
"""
projectName = "randomizePipeline"
organism = "bovine"
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info(
"This function compare the properties of 3 randomized functional annotations for a Bovine array."
)
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info("name of the project : %s " % projectName)
#Read rice microarray target sequence to define the set of gene products
fileName = "%s/ReferenceSet/%s.fasta" % (projectDir, organism)
refSet = RefSet(organism=organism, fileName=fileName, refType="Fasta")
#Read GO ontoloy
fileName = "%s/OBO/go_daily-termdb.obo-xml" % (projectDir)
G = readGOoboXML(fileName, force=False)
#Read Functional annotations
allFileName = list()
allFileName.append("%s/Annotation/Affy_%s.na31.annot.csv" %
(projectDir, organism))
allFileName.append("%s/Annotation/B2G_%s.annot" % (projectDir, organism))
allFileName.append("%s/Annotation/AID_%s.txt" % (projectDir, organism))
allPipeName = ["AFFY", "B2G", "AID"]
allFileType = allPipeName
pipeline = dict()
for pipeName, fileName, fileType in zip(allPipeName, allFileName,
allFileType):
FA = FuncAnnot(pipeName, refSet, G, organism=organism)
FA.read(fileName, fileType=fileType)
pipeline[pipeName] = FA
# Randomize FA
randomizeFA = RandomizeFA()
#-----------------------------------------------
# Shuffle functional annotation
batchList = ["shuffleAnnotation"]
batchExecute(batchList, randomizeFA,
[pipeline[pipeName] for pipeName in allPipeName])
#Analyse Functional annotations
analyseFA = AnalyseFA()
batchList = ["coherence", "redundancy"]
batchExecute(batchList, analyseFA,
[pipeline[pipeName] for pipeName in allPipeName])
#Export statistics to Excel
outDir = "%s/Export/%s" % (projectDir, organism)
createDir(outDir)
exportList = ["coherence", "redundancy"]
report = ReportFA(name="Randomize shuffle",
outDir=outDir,
organism=organism)
report.printStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
report.saveStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
#-----------------------------------------------
# Resample functional annotation
batchList = ["sampleAnnotation"]
batchExecute(batchList, randomizeFA,
[pipeline[pipeName] for pipeName in allPipeName])
#Analyse Functional annotations
#batchList=["obsolete", "unconnected", "removeUnconnected", "coverage", "richness", "numberAnnot", "coherence", "redundancy", "compactness", "specificity", "informationContent"]
batchList = [
"obsolete", "unconnected", "removeUnconnected", "coverage", "richness",
"numberAnnot", "redundancy", "specificity", "informationContent"
]
batchExecute(batchList, analyseFA,
[pipeline[pipeName] for pipeName in allPipeName])
#Export statistics to Excel
outDir = "%s/Export/%s" % (projectDir, organism)
createDir(outDir)
#exportList=["coverage", "numberAnnot", "richness", "coherence", "compactness", "specificity", "informationContent", "redundancy"]
exportList = [
"coverage", "numberAnnot", "richness", "specificity",
"informationContent", "redundancy"
]
report = ReportFA(name="Randomize sample",
outDir=outDir,
organism=organism)
report.printStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
report.saveStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info("")
def compareBovinePipelines(projectDir):
"""
This function compare the properties of 3 functional annotations for a Bovine array.
"""
projectName = "bovinePipeline"
organism = "bovine"
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info(
"This function compare the properties of 3 functional annotations for a Bovine array."
)
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info("name of the project : %s " % projectName)
#Read bovine microarray probe set to define the set of gene products
fileName = "%s/ReferenceSet/%s.fasta" % (projectDir, organism)
refSet = RefSet(organism=organism, fileName=fileName, refType="Fasta")
#Read GO ontoloy
fileName = "%s/OBO/go_daily-termdb.obo-xml" % (projectDir)
G = readGOoboXML(fileName, force=False)
#Read Functional annotations
allFileName = list()
allFileName.append("%s/Annotation/Affy_%s.na31.annot.csv" %
(projectDir, organism))
allFileName.append("%s/Annotation/B2G_%s.annot" % (projectDir, organism))
allFileName.append("%s/Annotation/AID_%s.txt" % (projectDir, organism))
allPipeName = ["AFFY", "B2G", "AID"]
allFileType = allPipeName
pipeline = dict()
for pipeName, fileName, fileType in zip(allPipeName, allFileName,
allFileType):
FA = FuncAnnot(pipeName, refSet, G, organism=organism)
FA.read(fileName, fileType=fileType)
pipeline[pipeName] = FA
#-----------------------------------------------
#Analyse Functional annotations
analyseFA = AnalyseFA()
#batchList=["obsolete", "unconnected", "removeUnconnected", "coverage", "richness", "numberAnnot", "coherence", "redundancy", "compactness", "specificity", "informationContent"]
batchList = [
"obsolete", "unconnected", "removeUnconnected", "coverage", "richness",
"numberAnnot", "redundancy", "specificity", "informationContent"
]
batchExecute(batchList, analyseFA,
[pipeline[pipeName] for pipeName in allPipeName])
#How big are the largest annotation sets ?
analyseFA.largestSet([pipeline[pipeName] for pipeName in allPipeName])
logger.info("The largest sets of annotations are :")
for pipeName in allPipeName:
FA = pipeline[pipeName]
logger.info("\t%d for %s" %
(FA['largestSet']['All_aspects_of_GO'], FA.name))
#Plot statistics of Functional annotations
outDir = "%s/Graph/%s" % (projectDir, organism)
createDir(outDir)
plotFA = PlotFA(xlabel="Annotation pipelines",
outDir=outDir,
name=projectName,
organism=organism,
ext="png")
batchExecute(batchList,
plotFA, [pipeline[pipeName] for pipeName in allPipeName],
doGrid=True)
#batchList=["coherenceHisto2D", "numberAnnotHisto2D"]
batchList = ["numberAnnotHisto2D"]
batchExecute(batchList,
plotFA, [pipeline[pipeName] for pipeName in allPipeName],
doGrid=True,
tit="")
#-----------------------------------------------
#Compare Functional annotations
compareFA = CompareFA()
batchList = ["venn", "funcSim"]
batchExecute(batchList, compareFA,
[pipeline[pipeName] for pipeName in allPipeName])
#Plot statistics of the comparison between Functional annotations
batchList = ["venn", "funcSymSim"]
batchExecute(batchList,
plotFA,
compareFA, [pipeline[pipeName] for pipeName in allPipeName],
tit="")
#-----------------------------------------------
#Export statistics to Excel
outDir = "%s/Export/%s" % (projectDir, organism)
createDir(outDir)
#exportList=["unconnected", "coverage", "richness", "numberAnnot", "coherence", "compactness", "specificity", "informationContent", "redundancy"]
exportList = [
"unconnected", "coverage", "richness", "numberAnnot", "specificity",
"informationContent", "redundancy"
]
reportFA = ReportFA(outDir=outDir, name=projectName, organism=organism)
reportFA.printStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
reportFA.saveStatistics([pipeline[pipeName] for pipeName in allPipeName],
exportList)
logger.info(
"◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦◦"
)
logger.info("")
from AIGO.ReferenceSet import RefSet
from AIGO.FunctionalAnnotation import FuncAnnot
from AIGO.go.OBO import readGOoboXML
from AIGO.Analyse import AnalyseFA
from AIGO.Report import ReportFA
from AIGO.utils.Execute import batchExecute
refSet = RefSet(organism="platypus",
fileName="platypus.refSet",
refType="Text")
G = readGOoboXML("go_daily-termdb.obo-xml")
FA = FuncAnnot("platypusProject", refSet, G, organism="platypus")
FA.read("platypus.gaf", "GAF")
analyseFA = AnalyseFA()
analyseFA.largestSet([FA])
logger.info("Largest sets of annotations:")
logger.info("\t%d for %s" % (FA['largestSet']['All_aspects_of_GO'], FA.name))
batchList = [
"coverage", "richness", "numberAnnot", "redundancy", "specificity",
"informationContent", "hPrecision"
]
batchExecute(batchList, analyseFA, [FA])
reportFA = ReportFA(outDir=None, name="platypusProject", organism="platypus")
reportFA.printStatistics([FA], batchList)