def main(argv):
version = "v0.17"
analysisName = ""
analysisFolder = ""
varianceSeed = 0.001
FDRLimit = 0.01
varianceSeedProvided = False
removeDuplicateUpper = False
tags = "!out"
outlierTag = "out"
logicOperatorsAsWords = False
dataFile = ""
relationsFile = ""
newRelFile = ""
removedRelFile = ""
defaultDataFile = "data"
defaultRelationsFile = "rels"
defaultTaggedRelFile = "tagged"
defaultNewRelFile = "cleaned"
defaultRemovedRelFile = "outliers"
defaultOutputInfo = "infoFile"
infoFile = ""
varFile = ""
defaultTableExtension = ".tsv"
defaultTextExtension = ".txt"
defaultGraphExtension = ".png"
verbose = True
oldWay = False # instead of tagging outliers, separating relations files, the old way
modeUsed = mode.onePerHigher
logList = [["SanXoTSieve " + version], ["Start: " + strftime("%Y-%m-%d %H:%M:%S")]]
try:
opts, args = getopt.getopt(argv, "a:p:v:d:r:n:L:V:f:ubDhH", ["analysis=", "folder=", "varianceseed=", "datafile=", "relfile=", "newrelfile=", "outlierrelfile=", "infofile=", "varfile=", "fdrlimit=", "one-to-one", "no-verbose", "randomise", "removeduplicateupper", "help", "advanced-help", "tags=", "outliertag=", "oldway", "word-operators"])
except getopt.GetoptError:
logList.append(["Error while getting parameters."])
stats.saveFile(infoFile, logList, "INFO FILE")
sys.exit(2)
if len(opts) == 0:
printHelp(version)
sys.exit()
for opt, arg in opts:
if opt in ("-a", "--analysis"):
analysisName = arg
if opt in ("-p", "--place", "--folder"):
analysisFolder = arg
if opt in ("-v", "--var", "--varianceseed"):
varianceSeed = float(arg)
varianceSeedProvided = True
elif opt in ("-d", "--datafile"):
dataFile = arg
elif opt in ("-r", "--relfile", "--relationsfile"):
relationsFile = arg
elif opt in ("-n", "--newrelfile"):
removedRelFile = arg
elif opt in ("-L", "--infofile"):
infoFile = arg
elif opt in ("-V", "--varfile"):
varFile = arg
elif opt in ("-u", "--one-to-one"):
modeUsed = mode.onlyOne
elif opt in ("-b", "--no-verbose"):
verbose = False
elif opt in ("--oldway"):
oldWay = True
elif opt in ("-f", "--fdrlimit"):
FDRLimit = float(arg)
elif opt in ("-D", "--removeduplicateupper"):
removeDuplicateUpper = True
elif opt in ("--tags"):
if arg.strip().lower() != "!out":
tags = "!out&(" + arg + ")"
elif opt in ("--word-operators"):
logicOperatorsAsWords = True
elif opt in ("--outliertag"):
outlierTag = "out"
elif opt in ("-h", "--help"):
printHelp(version)
sys.exit()
elif opt in ("-H", "--advanced-help"):
printHelp(version, advanced = True)
sys.exit()
# REGION: FILE NAMES SETUP
if len(analysisName) == 0:
if len(dataFile) > 0:
analysisName = os.path.splitext(os.path.basename(dataFile))[0]
else:
analysisName = defaultAnalysisName
if len(os.path.dirname(analysisName)) > 0:
analysisNameFirstPart = os.path.dirname(analysisName)
analysisName = os.path.basename(analysisName)
if len(analysisFolder) == 0:
analysisFolder = analysisNameFirstPart
if len(dataFile) > 0 and len(analysisFolder) == 0:
if len(os.path.dirname(dataFile)) > 0:
analysisFolder = os.path.dirname(dataFile)
# input
if len(dataFile) == 0:
dataFile = os.path.join(analysisFolder, analysisName + "_" + defaultDataFile + defaultTableExtension)
if len(os.path.dirname(dataFile)) == 0 and len(analysisFolder) > 0:
dataFile = os.path.join(analysisFolder, dataFile)
if len(os.path.dirname(varFile)) == 0 and len(os.path.basename(varFile)) > 0:
varFile = os.path.join(analysisFolder, varFile)
if len(varFile) > 0 and not varianceSeedProvided:
varianceSeed, varianceOk = stats.extractVarianceFromVarFile(varFile, verbose = verbose, defaultSeed = varianceSeed)
if not varianceOk:
logList.append(["Variance not found in text file."])
stats.saveFile(infoFile, logList, "INFO FILE")
sys.exit()
if len(relationsFile) == 0:
relationsFile = os.path.join(analysisFolder, analysisName + "_" + defaultRelationsFile + defaultTableExtension)
if len(os.path.dirname(relationsFile)) == 0:
relationsFile = os.path.join(analysisFolder, relationsFile)
# output
if len(newRelFile) == 0:
if oldWay: # suffix: "cleaned"
newRelFile = os.path.join(analysisFolder, analysisName + "_" + defaultNewRelFile + defaultTableExtension)
else: # suffix: "tagged"
newRelFile = os.path.join(analysisFolder, analysisName + "_" + defaultTaggedRelFile + defaultTableExtension)
if len(removedRelFile) == 0:
removedRelFile = os.path.join(analysisFolder, analysisName + "_" + defaultRemovedRelFile + defaultTableExtension)
if len(os.path.dirname(newRelFile)) == 0:
newRelFile = os.path.join(analysisFolder, newRelFile)
if len(os.path.dirname(removedRelFile)) == 0:
removedRelFile = os.path.join(analysisFolder, removedRelFile)
if len(infoFile) == 0:
infoFile = os.path.join(analysisFolder, analysisName + "_" + defaultOutputInfo + defaultTextExtension)
logList.append(["Variance seed = " + str(varianceSeed)])
logList.append(["Input data file: " + dataFile])
logList.append(["Input relations file: " + relationsFile])
if oldWay:
logList.append(["Output relations file without outliers: " + newRelFile])
logList.append(["Output relations file with outliers only: " + removedRelFile])
logList.append(["Removing duplicate higher level elements: " + str(removeDuplicateUpper)])
logList.append(["OldWay option activated: outliers are removed instead of tagged"])
else:
logList.append(["Relations file tagging outliers: " + newRelFile])
logList.append(["Tags to filter relations: " + tags])
logList.append(["Tag used for outliers: " + outlierTag])
# pp.pprint(logList)
# sys.exit()
# END REGION: FILE NAMES SETUP
relations = stats.loadRelationsFile(relationsFile)
data = stats.loadInputDataFile(dataFile)
if oldWay:
# only for backward compatibility. Note that tags are not supported
newRelations, removedRelations, logResults = \
getRelationsWithoutOutliers(data,
relations,
varianceSeed,
FDRLimit = FDRLimit,
modeUsed = modeUsed,
removeDuplicateUpper = removeDuplicateUpper)
else:
newRelations, removedRelations, logResults = \
tagRelationsWithoutOutliers(data,
relations,
varianceSeed,
FDRLimit = FDRLimit,
modeUsed = modeUsed,
removeDuplicateUpper = removeDuplicateUpper,
tags = tags,
outlierTag = outlierTag,
logicOperatorsAsWords = logicOperatorsAsWords)
if oldWay:
stats.saveFile(newRelFile, newRelations, "idsup\tidinf")
else:
stats.saveFile(newRelFile, newRelations, "idsup\tidinf\ttags")
stats.saveFile(infoFile, logList, "INFO FILE")
if oldWay:
stats.saveFile(removedRelFile, removedRelations, "idsup\tidinf")
def associateElements(inStats = "", uFile = "", relFile = ""):
results = []
relations = stats.loadRelationsFile(relFile)
relations = stats.sortByIndex(relations, 0)
statsData = stats.loadStatsDataFile(inStats)
ZijList = []
for element in statsData:
ZijList.append([element[3], element[7]])
theorList = []
experList = []
N = len(ZijList)
for i in range(N):
theorList.append([ZijList[i][0], ZijList[i][1], norm.cdf(float(ZijList[i][1]))])
experList.append([ZijList[i][0], ZijList[i][1], (float(i) + 0.5) / float(N)])
higherElements = stats.load2stringList(uFile, removeCommas = True)
# WARNING! higherElements must be a list of lists
# with each sublist being id, n, Z, FDR, X
# begin: jmrc
if not higherElements:
sms = "ERROR: higherElements is empty. The higherElements must be a list of lists with each sublist being id, n, Z, FDR, X"
sys.exit(sms)
# end: jmrc
elementList = []
if higherElements[0] == ['id', 'Z', 'n']:
# this means the list comes from SanXoTSqueezer
# so the header and the extra columns have to be removed
for element in higherElements[1:]:
# switch to id, n, Z, FDR
elementList.append([element[0], element[2], element[1], float("nan"), float("nan")])
if higherElements[0] == ['id', 'n', 'Z', 'FDR']:
# this means it does not contain X, so a nan is put on its place
for element in higherElements[1:]:
elementList.append([element[0], element[1], element[2], element[3], float("nan")])
if higherElements[0] == ['id', 'n', 'Z', 'FDR', 'X']:
for element in higherElements[1:]:
elementList.append([element[0], element[1], element[2], element[3], element[4]])
# otherwise
if higherElements[0] != ['id', 'Z', 'n'] and higherElements[0] != ['id', 'n', 'Z', 'FDR'] and higherElements[0] != ['id', 'n', 'Z', 'FDR', 'X']:
for element in higherElements:
elementList.append([element[0], float("nan"), float("nan"), float("nan"), float("nan")])
statsData = stats.sortByIndex(statsData, 7)
relationsFirstColumn = stats.extractColumns(relations, 0)
relationsSecondColumn = stats.extractColumns(relations, 1)
experListFirstColumn = stats.extractColumns(experList, 0)
for uElement in elementList:
lowerElementList = []
first = stats.firstIndex(relationsFirstColumn, uElement[0])
if first > -1: # -1 means it is not in the list
notInList = 0
last = stats.lastIndex(relationsFirstColumn, uElement[0])
lowerElements = relationsSecondColumn[first:last + 1] # "+1" is to include the last one
for element in lowerElements:
lowerIndex = stats.firstIndex(experListFirstColumn, element)
if lowerIndex > -1: # -1 means it is not in the list
lowerElementList.append(element)
else:
notInList += 1
lowerElementList = stats.sortByIndex(lowerElementList, 0)
results.append([uElement[0], lowerElementList])
else:
if len(uElement[0].strip()) > 0:
results.append([uElement[0], None])
return results, elementList, ""
def calibrate(inputRawData=None,
inputRelations=None,
rawDataFile="",
relationsFile="",
kSeed=1,
varianceSeed=0.001,
medianSide=100,
maxIterations=0,
verbose=False,
showGraph=False,
showSumSQ=False,
forceParameters=False,
alphaSeed=1.0,
showRank=True,
useCooperativity=False,
graphFileVRank="",
graphFileVValue="",
graphDataFile="",
graphDPI=None):
extraLog = []
extraLog.append([])
if verbose:
print()
print("loading input raw data file")
extraLog.append(["loading input raw data file"])
if inputRawData == None:
if len(rawDataFile) == 0:
print("Error: no input raw data")
extraLog.append(["Error: no input raw data"])
else:
inputRawData = stats.loadInputDataFile(rawDataFile)
if verbose:
print("loading relations file")
extraLog.append(["loading relations file"])
if inputRelations == None:
if len(relationsFile) == 0:
if not forceParameters:
print("Error: no relations file")
extraLog.append(["Error: no relations file"])
return None, None, None, None, extraLog
else:
inputRelations = stats.loadRelationsFile(relationsFile)
#### calculate k and variance ####
alpha = 1.0
if not forceParameters:
if verbose:
print("calculating K and variance")
extraLog.append(["calculating K and variance"])
# *** just to see it
result = getKandVariance(inputRawData,
inputRelations,
kSeed=kSeed,
varianceSeed=varianceSeed,
maxIterations=maxIterations,
verbose=True,
showSumSQ=True,
medianSide=medianSide,
alphaSeed=alphaSeed,
useCooperativity=useCooperativity)
k = result[0]
variance = result[1]
if useCooperativity: alpha = result[2]
else:
k = kSeed
variance = varianceSeed
alpha = alphaSeed
# save VRank graph
showGraphTool(inputRawData,
inputRelations,
k,
variance,
alpha,
medianSide,
showRank=True,
graphFile=graphFileVRank,
graphData=graphDataFile,
dpi=graphDPI,
showGraph=showGraph)
# save VValue graph
showGraphTool(inputRawData,
inputRelations,
k,
variance,
alpha,
medianSide,
showRank=False,
graphFile=graphFileVValue,
dpi=graphDPI,
showGraph=showGraph)
# get calibrated idXV
idXV = idXVcal(inputRawData, k, alpha)
return idXV, variance, k, alpha, extraLog
def calibrate(inputRawData = None,
inputRelations = None,
rawDataFile = "",
relationsFile = "",
kSeed = 1,
varianceSeed = 0.001,
medianSide = 100,
maxIterations = 0,
verbose = False,
showGraph = False,
showSumSQ = False,
forceParameters = False,
alphaSeed = 1.0,
showRank = True,
useCooperativity = False,
graphFileVRank = "",
graphFileVValue = "",
graphDataFile = "",
graphDPI = None):
extraLog = []
extraLog.append([])
if verbose:
print()
print("loading input raw data file")
extraLog.append(["loading input raw data file"])
if inputRawData == None:
if len(rawDataFile) == 0:
print("Error: no input raw data")
extraLog.append(["Error: no input raw data"])
else:
inputRawData = stats.loadInputDataFile(rawDataFile)
if verbose:
print("loading relations file")
extraLog.append(["loading relations file"])
if inputRelations == None:
if len(relationsFile) == 0:
if not forceParameters:
print("Error: no relations file")
extraLog.append(["Error: no relations file"])
return None, None, None, None, extraLog
else:
inputRelations = stats.loadRelationsFile(relationsFile)
#### calculate k and variance ####
alpha = 1.0
if not forceParameters:
if verbose:
print("calculating K and variance")
extraLog.append(["calculating K and variance"])
# *** just to see it
result = getKandVariance(inputRawData, inputRelations, kSeed = kSeed, varianceSeed = varianceSeed, maxIterations = maxIterations, verbose = True, showSumSQ = True, medianSide = medianSide, alphaSeed = alphaSeed, useCooperativity = useCooperativity)
k = result[0]
variance = result[1]
if useCooperativity: alpha = result[2]
else:
k = kSeed
variance = varianceSeed
alpha = alphaSeed
# save VRank graph
showGraphTool(inputRawData, inputRelations, k, variance, alpha, medianSide, showRank = True, graphFile = graphFileVRank, graphData = graphDataFile, dpi = graphDPI, showGraph = showGraph)
# save VValue graph
showGraphTool(inputRawData, inputRelations, k, variance, alpha, medianSide, showRank = False, graphFile = graphFileVValue, dpi = graphDPI, showGraph = showGraph)
# get calibrated idXV
idXV = idXVcal(inputRawData, k, alpha)
return idXV, variance, k, alpha, extraLog
def main(argv):
version = "v0.17"
analysisName = ""
analysisFolder = ""
varianceSeed = 0.001
FDRLimit = 0.01
varianceSeedProvided = False
removeDuplicateUpper = False
tags = "!out"
outlierTag = "out"
logicOperatorsAsWords = False
dataFile = ""
relationsFile = ""
newRelFile = ""
removedRelFile = ""
defaultDataFile = "data"
defaultRelationsFile = "rels"
defaultTaggedRelFile = "tagged"
defaultNewRelFile = "cleaned"
defaultRemovedRelFile = "outliers"
defaultOutputInfo = "infoFile"
infoFile = ""
varFile = ""
defaultTableExtension = ".tsv"
defaultTextExtension = ".txt"
defaultGraphExtension = ".png"
verbose = True
oldWay = False # instead of tagging outliers, separating relations files, the old way
modeUsed = mode.onePerHigher
logList = [["SanXoTSieve " + version],
["Start: " + strftime("%Y-%m-%d %H:%M:%S")]]
try:
opts, args = getopt.getopt(argv, "a:p:v:d:r:n:L:V:f:ubDhH", [
"analysis=", "folder=", "varianceseed=", "datafile=", "relfile=",
"newrelfile=", "outlierrelfile=", "infofile=", "varfile=",
"fdrlimit=", "one-to-one", "no-verbose", "randomise",
"removeduplicateupper", "help", "advanced-help", "tags=",
"outliertag=", "oldway", "word-operators"
])
except getopt.GetoptError:
logList.append(["Error while getting parameters."])
stats.saveFile(infoFile, logList, "INFO FILE")
sys.exit(2)
if len(opts) == 0:
printHelp(version)
sys.exit()
for opt, arg in opts:
if opt in ("-a", "--analysis"):
analysisName = arg
if opt in ("-p", "--place", "--folder"):
analysisFolder = arg
if opt in ("-v", "--var", "--varianceseed"):
varianceSeed = float(arg)
varianceSeedProvided = True
elif opt in ("-d", "--datafile"):
dataFile = arg
elif opt in ("-r", "--relfile", "--relationsfile"):
relationsFile = arg
elif opt in ("-n", "--newrelfile"):
removedRelFile = arg
elif opt in ("-L", "--infofile"):
infoFile = arg
elif opt in ("-V", "--varfile"):
varFile = arg
elif opt in ("-u", "--one-to-one"):
modeUsed = mode.onlyOne
elif opt in ("-b", "--no-verbose"):
verbose = False
elif opt in ("--oldway"):
oldWay = True
elif opt in ("-f", "--fdrlimit"):
FDRLimit = float(arg)
elif opt in ("-D", "--removeduplicateupper"):
removeDuplicateUpper = True
elif opt in ("--tags"):
if arg.strip().lower() != "!out":
tags = "!out&(" + arg + ")"
elif opt in ("--word-operators"):
logicOperatorsAsWords = True
elif opt in ("--outliertag"):
outlierTag = "out"
elif opt in ("-h", "--help"):
printHelp(version)
sys.exit()
elif opt in ("-H", "--advanced-help"):
printHelp(version, advanced=True)
sys.exit()
# REGION: FILE NAMES SETUP
if len(analysisName) == 0:
if len(dataFile) > 0:
analysisName = os.path.splitext(os.path.basename(dataFile))[0]
else:
analysisName = defaultAnalysisName
if len(os.path.dirname(analysisName)) > 0:
analysisNameFirstPart = os.path.dirname(analysisName)
analysisName = os.path.basename(analysisName)
if len(analysisFolder) == 0:
analysisFolder = analysisNameFirstPart
if len(dataFile) > 0 and len(analysisFolder) == 0:
if len(os.path.dirname(dataFile)) > 0:
analysisFolder = os.path.dirname(dataFile)
# input
if len(dataFile) == 0:
dataFile = os.path.join(
analysisFolder,
analysisName + "_" + defaultDataFile + defaultTableExtension)
if len(os.path.dirname(dataFile)) == 0 and len(analysisFolder) > 0:
dataFile = os.path.join(analysisFolder, dataFile)
if len(os.path.dirname(varFile)) == 0 and len(
os.path.basename(varFile)) > 0:
varFile = os.path.join(analysisFolder, varFile)
if len(varFile) > 0 and not varianceSeedProvided:
varianceSeed, varianceOk = stats.extractVarianceFromVarFile(
varFile, verbose=verbose, defaultSeed=varianceSeed)
if not varianceOk:
logList.append(["Variance not found in text file."])
stats.saveFile(infoFile, logList, "INFO FILE")
sys.exit()
if len(relationsFile) == 0:
relationsFile = os.path.join(
analysisFolder,
analysisName + "_" + defaultRelationsFile + defaultTableExtension)
if len(os.path.dirname(relationsFile)) == 0:
relationsFile = os.path.join(analysisFolder, relationsFile)
# output
if len(newRelFile) == 0:
if oldWay: # suffix: "cleaned"
newRelFile = os.path.join(
analysisFolder,
analysisName + "_" + defaultNewRelFile + defaultTableExtension)
else: # suffix: "tagged"
newRelFile = os.path.join(
analysisFolder, analysisName + "_" + defaultTaggedRelFile +
defaultTableExtension)
if len(removedRelFile) == 0:
removedRelFile = os.path.join(
analysisFolder,
analysisName + "_" + defaultRemovedRelFile + defaultTableExtension)
if len(os.path.dirname(newRelFile)) == 0:
newRelFile = os.path.join(analysisFolder, newRelFile)
if len(os.path.dirname(removedRelFile)) == 0:
removedRelFile = os.path.join(analysisFolder, removedRelFile)
if len(infoFile) == 0:
infoFile = os.path.join(
analysisFolder,
analysisName + "_" + defaultOutputInfo + defaultTextExtension)
logList.append(["Variance seed = " + str(varianceSeed)])
logList.append(["Input data file: " + dataFile])
logList.append(["Input relations file: " + relationsFile])
if oldWay:
logList.append(
["Output relations file without outliers: " + newRelFile])
logList.append(
["Output relations file with outliers only: " + removedRelFile])
logList.append([
"Removing duplicate higher level elements: " +
str(removeDuplicateUpper)
])
logList.append([
"OldWay option activated: outliers are removed instead of tagged"
])
else:
logList.append(["Relations file tagging outliers: " + newRelFile])
logList.append(["Tags to filter relations: " + tags])
logList.append(["Tag used for outliers: " + outlierTag])
# pp.pprint(logList)
# sys.exit()
# END REGION: FILE NAMES SETUP
relations = stats.loadRelationsFile(relationsFile)
data = stats.loadInputDataFile(dataFile)
if oldWay:
# only for backward compatibility. Note that tags are not supported
newRelations, removedRelations, logResults = \
getRelationsWithoutOutliers(data,
relations,
varianceSeed,
FDRLimit = FDRLimit,
modeUsed = modeUsed,
removeDuplicateUpper = removeDuplicateUpper)
else:
newRelations, removedRelations, logResults = \
tagRelationsWithoutOutliers(data,
relations,
varianceSeed,
FDRLimit = FDRLimit,
modeUsed = modeUsed,
removeDuplicateUpper = removeDuplicateUpper,
tags = tags,
outlierTag = outlierTag,
logicOperatorsAsWords = logicOperatorsAsWords)
if oldWay:
stats.saveFile(newRelFile, newRelations, "idsup\tidinf")
else:
stats.saveFile(newRelFile, newRelations, "idsup\tidinf\ttags")
stats.saveFile(infoFile, logList, "INFO FILE")
if oldWay:
stats.saveFile(removedRelFile, removedRelations, "idsup\tidinf")
def ZpCalculator(relationsFile, modifiedPeptidesFile, modifiedPeptidesFile1,
nonModifiedPep2ProtFile, pep2protein, outname, varFile,
varFile1):
folderout = outname.split("\\")
OutfolderLocation = "\\".join(folderout[:-1])
norm_wo_V = []
norm_wo_W = []
main_file = []
main_file1 = []
main_file_FINAL = []
Norm_V = [["idinf", "\t", "X'inf", "\t", "Vinf", "\n"]]
Norm_W = [["idinf", "\t", "X'inf", "\t", "Winf", "\n"]]
W = open(OutfolderLocation + "\peptide2protein_Norm_W.txt", "w")
V = open(OutfolderLocation + "\peptide2protein_Norm_V.txt", "w")
dic_seq = stats.load2dictionary(
modifiedPeptidesFile, keyNum=0, n1=1,
n2=2) ################# wo file ###########
dic_dis = stats.load2dictionary(
nonModifiedPep2ProtFile, keyNum=3, n1=1, n2=4, n3=5,
n4=6) ######### protein to all file ########
dic_seq1 = stats.load2dictionary(modifiedPeptidesFile1,
keyNum=0,
n1=1,
n2=2) ################ W file #########
W_seq = stats.load2dictionary(pep2protein, keyNum=0,
n1=6) ###nonmod peptide to protein####
#print W_seq
#pdb.set_trace()
variance, varianceOk = stats.extractVarianceFromVarFile(varFile)
variance1, variance1Ok = stats.extractVarianceFromVarFile(varFile1)
x = float(dic_dis[dic_dis.keys()[0]][0])
x_meanCount = int(dic_dis[dic_dis.keys()[3]][3])
rel_seq = {}
count_n = {}
count_n1 = {}
allRelations = stats.loadRelationsFile(relationsFile)
## to count the descriptions in same for different sequence
### all the calculation for ......WO files........ ##########
for relation in allRelations:
Rel_sequence = relation[1]
Rel_discription = relation[0] # protein
rel_seq[Rel_sequence] = Rel_discription
if Rel_sequence in dic_seq:
if rel_seq[Rel_sequence] not in count_n:
count_n[rel_seq[Rel_sequence]] = 1
elif rel_seq[Rel_sequence] in count_n:
count_n[rel_seq[Rel_sequence]] += 1
for relation_n in allRelations:
Rel_sequence = relation_n[1]
Rel_discription = relation_n[0] # protein
if Rel_sequence in dic_seq:
count = count_n[rel_seq[Rel_sequence]]
xp = float(dic_seq[Rel_sequence][0])
vp = float(dic_seq[Rel_sequence][1])
NormV_VP = float(dic_seq[Rel_sequence][1])
main_file_des = rel_seq[Rel_sequence]
wp = float(1 / (1 / vp + variance)) ###(1 / ((1 / vp) + variance))
wq = float(1 / (1 / wp + variance1))
xq = "0"
vq = "1"
x_mean = x
NormV_xp = float(xp - x_mean)
NormW_VP = wq
zp = (xp - x_mean) * (math.sqrt(wq)) * math.sqrt(x_meanCount /
(x_meanCount - 1))
#print zp
norm_wo_V.append([
str(Rel_sequence), "\t",
str(NormV_xp), "\t",
str(NormV_VP), "\n"
])
norm_wo_W.append([
str(Rel_sequence), "\t",
str(NormV_xp), "\t",
str(NormW_VP), "\n"
])
main_file.append(
[main_file_des, xq, vq, Rel_sequence, xp, vp, x_meanCount, zp])
################...........W files ..........############################
for relation_n in allRelations:
Rel_sequence = relation_n[1]
Rel_discription = relation_n[0]
if Rel_sequence in dic_seq1:
#count1=count_n1[rel_seq[Rel_sequence]]
xp1 = float(dic_seq1[Rel_sequence][0])
vp1 = float(dic_seq1[Rel_sequence][1])
w_NormV_VP = float(dic_seq1[Rel_sequence][1])
main_file_des1 = rel_seq[Rel_sequence]
#pdb.set_trace()
#pdb.set_trace()
wp1 = float(1 /
(1 / vp1 + variance)) ###(1 / ((1 / vp) + variance))
wq1 = float(1 / (1 / wp1 + variance1))
w_NormW_VP = wp1
if main_file_des1 in dic_dis and main_file_des1 in W_seq:
#print W_seq[main_file_des1]
count1 = int(W_seq[main_file_des1][0])
#count1=int(W_seq[">sp|O00602|FCN1_HUMAN Ficolin-1 OS=H**o sapiens GN=FCN1 PE=1 SV=2"][0])
#print count1
#pdb.set_trace()
#n=int(count1)
xq1 = float(dic_dis[main_file_des1][1])
vq1 = float(dic_dis[main_file_des1][2])
w_NormV_xp = float(xp1 - xq1)
if count1 > 1:
zp1 = (xp1 - xq1) * (math.sqrt(wp1)) * math.sqrt(
count1 / (count1 - 1))
else:
zp1 = "nan"
main_file1.append([
main_file_des1, xq1, vq1, Rel_sequence, xp1, vp1, count1,
zp1
]) #population_A_B()
main_file_FINAL = main_file + main_file1
Norm_V.append([
str(Rel_sequence), "\t",
str(w_NormV_xp), "\t",
str(w_NormV_VP), "\n"
])
Norm_W.append([
str(Rel_sequence), "\t",
str(w_NormV_xp), "\t",
str(w_NormW_VP), "\n"
])
#pdb.set_trace()
#print len(Norm_V)
#print len(Norm_W)
## merge another stas file:
nonMod_V = []
nonMod_W = []
List_V = []
List_W = []
with open(pep2protein) as file_new:
next(file_new)
for line in file_new:
n_splits = line.split("\t")
nonNormV_xp = (float(n_splits[4]) - float(n_splits[1]))
nonNormV_VP = (1 / (1 / float(n_splits[5]) + variance))
#nonNormW_VP = (1 / (1/float(n_splits[5]) + variance))
nonRel_sequence = str(n_splits[3])
nonMod_V.append([
str(nonRel_sequence), "\t",
str(nonNormV_xp), "\t",
str(nonNormV_VP), "\n"
])
#nonMod_W=[str(nonRel_sequence),"\t", str(nonNormV_xp),"\t", str(nonNormW_VP),"\n"]
main_file_FINAL.append([
n_splits[0], n_splits[1], n_splits[2], n_splits[3],
n_splits[4], n_splits[5], n_splits[6],
float(n_splits[7])
])
#print len(nonMod_V)
List_V = Norm_V + norm_wo_V + nonMod_V
#print len(List_V)
for lineV in List_V:
V.writelines(lineV)
V.close()
List_W = Norm_W + norm_wo_W + nonMod_V
for lineW in List_W:
W.writelines(lineW)
W.close()
mainlist = stats.fdr_calculator(main_file_FINAL)
return mainlist