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mrmTools.py
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mrmTools.py
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# -*- coding: utf-8 -*-
"""
Created on Mon May 5 18:23:21 2014
@author: jhdavis
"""
import numpy
import qMS
import vizLib
import matplotlib.pyplot as plt
import pylab
from matplotlib import gridspec
import pandas
import qMSDefs
def calcErrorMRM(dataFrame):
dataFrame['error'] = abs((dataFrame['light Area']/dataFrame['heavy Area']) - (dataFrame['light TotalArea'])/(dataFrame['heavy TotalArea']))
dataFrame['errorFrac'] = dataFrame['error']/(dataFrame['light TotalArea']/dataFrame['heavy TotalArea'])
dataFrame['RTOffset'] = dataFrame['light RetentionTime'] - dataFrame['heavy RetentionTime']
dataFrame['light FracTotalArea'] = dataFrame['light Area']/(dataFrame['light TotalArea'])
dataFrame['heavy FracTotalArea'] = dataFrame['heavy Area']/(dataFrame['heavy TotalArea'])
return dataFrame
def scoreDatasetsMRM(df, lppml, lppmh, hppml, hppmh,
la, ha,
dp, fal, fah,
e, ef, rtoL, rtoH):
pandas.options.mode.chained_assignment = None
df['score'] = 0
df['score'] = df['score'] + ((df['light MassErrorPPM'] > lppml) & (df['light MassErrorPPM'] < lppmh))*1 + \
((df['heavy MassErrorPPM'] > hppml) & (df['heavy MassErrorPPM'] < hppmh))*1 + \
(df['light Area'] > la)*1 + \
(df['heavy Area'] > ha)*1 + \
(df['DotProductLightToHeavy'] > dp)*1 + \
(df['light FracTotalArea'] > fal)*1 + \
(df['heavy FracTotalArea'] > fah)*1 + \
(df['error'] < e)*1 + \
(df['errorFrac'] < ef)*1 + \
((df['RTOffset'] > rtoL) & (df['RTOffset'] < rtoH))*1
return df
def correctOccupancyMRM(ref, toCorr, maxZero=True):
corr = {}
for p in ref.keys():
med = numpy.median(ref[p])
if maxZero:
corr[p] = numpy.array([max(i-med, 0.0) for i in toCorr[p]])
else:
corr[p] = numpy.array([i-med for i in toCorr[p]])
return corr
def correctStatsDictDictMRM(sdd, ref):
corrSdd = {}
for k in sdd.keys():
corrSdd[k] = correctOccupancyMRM(ref, sdd[k])
return corrSdd
def normStatsDictMRM(sd, normValue=1.0, normProtein=None):
normSD = {}
if not normProtein==None:
normValue = 1.0/numpy.median(sd[normProtein])
for p in sd:
normSD[p] = numpy.array([i*normValue for i in sd[p]])
return normSD
def normStatsDictDictMRM(sdd, normValue=1.0, normProtein=None):
normSDD = {}
for k in sdd.keys():
normSDD[k] = normStatsDictMRM(sdd[k], normValue, normProtein)
return normSDD
def makeStatsDictMRM(dataFrame, fileName, field = 'currentCalc', proteinList = None, filterField = 'allClear'):
if proteinList is None:
proteinList = list(dataFrame['Protein Name'].unique())
pDict = {}
fileFrame = dataFrame[dataFrame['File Name'] == fileName]
for p in proteinList:
pDict[p] = fileFrame[(fileFrame['Protein Name'] == p) & (fileFrame[filterField])][field].values
return pDict
def makeFileStatsDictMRM(dataFrame, listOfFiles=None, field = 'currentCalc', proteinList = None, filterField = 'allClear'):
if listOfFiles is None:
listOfFiles = list(dataFrame['File Name'].unique())
filePDict = {}
for i in listOfFiles:
filePDict[i] = makeStatsDictMRM(dataFrame, i, field=field, proteinList = proteinList, filterField = filterField)
return filePDict
def getAllOccupancyMRM(dataFrame, fileName, listOfProteins=None,
num=['light'], den=['heavy'], total=False,
normProtein=None, normValue=1.0, offset=0.0, selectField='File Name'):
if listOfProteins is None:
listOfProteins = list(dataFrame['Protein Name'].unique())
pDict = {}
for p in listOfProteins:
pDict[p] = getOccupancyMRM(dataFrame, fileName, p, num=num, den=den,
total=total, normValue=normValue,
offset=offset, allData=False, selectField=selectField).values
if not (normProtein is None):
normValue = 1/numpy.median(pDict[normProtein])
for p in listOfProteins:
pDict[p] = getOccupancyMRM(dataFrame, fileName, p, num=num, den=den,
total=total, normValue=normValue,
offset=offset, allData=False, selectField=selectField).values
return pDict
def getAllOccupancyFileListMRM(dataFrame, fileList, listOfProteins=None,
num=['light'], den=['heavy'], total=False,
normProtein=None, normValue=1.0, offset=0.0, selectField='File Name'):
filePDict = {}
for i in fileList:
filePDict[i] = getAllOccupancyMRM(dataFrame, i, listOfProteins=listOfProteins,
num=num, den=den, total=total, normProtein=normProtein,
normValue=normValue, offset=offset, selectField=selectField)
return filePDict
def getOccupancyMRM(dataFrame, fileName, proteinName, num=['light'], den=['heavy'], normValue=1.0, offset=0.0, total=False, allData=False, selectField='File Name'):
"""getOccupancy takes a pandas dataframe generated from reading an MRM CSV file
as well as a fileName (eg.wiff) and a a proteinName. It calculates a
protein occupancy using the fields specified in the numerator and
denominator lists.
:param dataFrame: the dataFrame to work on. Should be generated from a
Skyline MRM export results CSV (e.g. dataFrame = pandas.read_csv(csvFile, na_values='#N/A'))
Must bear the columns "FileName, ProteinName, Area, TotalArea
:type dataFrame: a pandas dataframe
:param fileName: the file to consider (the wiff file)
:type fileName: a string
:param proteinName: the protein to consider
:type proteinName: a string
:param proteinName: the protein to consider
:type proteinName: a string
:param num: species to calc in the numerator
:type num: a list of strings (light or heavy)
:param den: species to calc in the denominator
:type den: a list of strings (light or heavy)
:param total: boolean to calculate the total or on a product by product basis
:type total: boolean (defaults to false)
:param allData: a boolean to return the full dataframe or just the calculated value
:type allData: boolean (defaults to false)
:returns: a pandas dataframe with the calculated value (either appended or on its own)
"""
allProducts = dataFrame[(dataFrame[selectField]==fileName) & (dataFrame['Protein Name']==proteinName)]
if total:
stringAppend = ' Total Area'
else:
stringAppend = ' Area'
allProducts['num'] = 0.0
allProducts['den'] = 0.0
for i in num:
allProducts['num'] = allProducts['num'] + allProducts[i + stringAppend]
for i in den:
allProducts['den'] = allProducts['den'] + allProducts[i + stringAppend]
allProducts['calcValue'] = (allProducts['num']/allProducts['den'])*normValue+offset
if allData:
return allProducts
else:
if total:
return qMS.dropDuplicatesPandas(allProducts)['calcValue'].dropna()
else:
return allProducts['calcValue'].dropna()
def getInfoMRM(dataFrame, index):
"""getInfoMRM takes a pandas dataframe (read from the tsv output from skyline),
and an index into that dataframe. Returns info related to that index
in the order [filename, mod. pep. seq, precharge, fragIon, prodCharge, isotope]
:param dataFrame: pandas dataFrame - generated using tsv from skyline and the command
dataFrame = pandas.read_csv(file, na_values='#N/A', sep='\t')
:type dataFrame: pandas dataframe
:param index: a int of what index to inspect
:type index: int
:returns: a list of data, [filename, mod. pep. seq, precharge, fragIon, prodCharge, isotope]
"""
fn = dataFrame.ix[index]['FileName']
pepSeq = dataFrame.ix[index]['Peptide Modified Sequence']
precursorCharge = dataFrame.ix[index]['Precursor Charge']
fragIon = dataFrame.ix[index]['Fragment Ion']
prodCharge = dataFrame.ix[index]['Product Charge']
isotope = dataFrame.ix[index]['IsotopeLabel Type']
return [fn, pepSeq, precursorCharge, fragIon, prodCharge, isotope]
def getTotalChromatographMRM(dataFrame, index, fragIon=None):
[fn, pms, preC, fi, proC, isotope] = getInfoMRM(dataFrame, index)
subFrame = dataFrame[(dataFrame['File Name'] == fn) &
(dataFrame['Peptide Modified Sequence'] == pms) &
(dataFrame['Precursor Charge'] == preC) &
(dataFrame['Isotope Label Type'] == isotope)]
if fragIon is None:
first = subFrame['Intensities'].values[0].split(',')
totArray = numpy.array([float(i) for i in first])
for a in subFrame['Intensities'].values[1:]:
b = numpy.array([float(x) for x in a.split(',')])
totArray = numpy.add(totArray,b)
else:
print "NEED TO WRITE THIS CODE TO DEAL WITH SUBSETS OF FRAGMENT IONS"
return [numpy.array([float(x) for x in subFrame['Times'].values[0].split(',')]), totArray]
def getPairedIonMRM(dataFrame, index):
[fn, pms, preC, fi, proC, isotope] = getInfoMRM(dataFrame, index)
if isotope == 'light':
isotope = 'heavy'
else:
isotope = 'light'
return dataFrame[(dataFrame['File Name'] == fn) &
(dataFrame['Peptide Modified Sequence'] == pms) &
(dataFrame['Precursor Charge'] == preC) &
(dataFrame['Fragment Ion'] == fi) &
(dataFrame['Product Charge'] == proC) &
(dataFrame['Isotope Label Type'] == isotope)].index.values[0]
def getIsotopPairTotalsMRM(dataFrame, index):
[fn, pms, preC, fi, proC, isotope] = getInfoMRM(dataFrame, index)
indexOpp = getPairedIonMRM(dataFrame, index)
if isotope == 'light':
[lX, lY] = getTotalChromatographMRM(dataFrame, index)
[hX, hY] = getTotalChromatographMRM(dataFrame, indexOpp)
else:
[lX, lY] = getTotalChromatographMRM(dataFrame, indexOpp)
[hX, hY] = getTotalChromatographMRM(dataFrame, index)
return [lX, lY, hX, hY]
def getRelatedIndeciesMRM(dataFrame, index):
[fn, ps, preC, fi, proC, i] = getInfoMRM(dataFrame, index)
return dataFrame[(dataFrame['Isotope Label Type'] == i) &
(dataFrame['File Name'] == fn) &
(dataFrame['Peptide Modified Sequence'] == ps) &
(dataFrame['Precursor Charge'] == preC)].index.values
def plotTotalChromPairsMRM(dataFrame, toPlotIndex, axis,
colors=['blue', 'red'], smooth=0, zoom=False):
[lightX, lightY, heavyX, heavyY] = getIsotopPairTotalsMRM(dataFrame, toPlotIndex)
if smooth > 0:
lightY = vizLib.smoothListGaussian([float(i) for i in lightY], degree=smooth)
heavyY = vizLib.smoothListGaussian([float(i) for i in heavyY], degree=smooth)
axis.plot(lightX, lightY, colors[0], label='light')
axis.plot(heavyX, heavyY, colors[1], label='heavy')
if zoom:
maxIndex = numpy.argmax(numpy.array(lightY))
axis.set_xlim(lightX[maxIndex]-2, lightX[maxIndex]+2)
return axis
def plotAllTransitionsMRM(dataFrame, index, a, colors=None, smooth=0, zoom=False):
allIndicies = getRelatedIndeciesMRM(dataFrame, index)
colors = vizLib.getBCs('q', min(len(allIndicies), 9))
colors.append('grey')
for i in range(len(allIndicies)):
[fn, ps, preC, fi, proC, iso] = getInfoMRM(dataFrame, allIndicies[i])
a = plotMRM_index(dataFrame, allIndicies[i], a, color=colors[i],
smooth=smooth, zoom=zoom)
return a
def plotMRM(dataFrame, fileName, pepSeq, precursorCharge, fragIon, prodCharge,
isotopeLabel, ax, color='grey', smooth=0, zoom=False):
subDF = dataFrame[(dataFrame['Peptide Modified Sequence'] == pepSeq) &
(dataFrame['Fragment Ion'] == fragIon) &
(dataFrame['Precursor Charge'] == precursorCharge) &
(dataFrame['Isotope Label Type'] == isotopeLabel) &
(dataFrame['Product Charge'] == prodCharge) &
(dataFrame['File Name'] == fileName)]
X = subDF['Times'].values[0].split(',')
Y = subDF['Intensities'].values[0].split(',')
if smooth > 0:
Y = vizLib.smoothListGaussian([float(i) for i in Y], degree=smooth)
ax.plot(X, Y, color=color)
if zoom:
if numpy.max(Y) > 100:
maxIndex = numpy.argmax(numpy.array(Y))
ax.set_xlim(float(X[maxIndex])-2, float(X[maxIndex])+2)
return ax
def plotMRM_index(dataFrame, index, ax, color='grey', smooth=0, zoom=False):
[fn, pms, preC, fi, proC, isotope] = getInfoMRM(dataFrame, index)
plotMRM(dataFrame, fn, pms, preC, fi, proC, isotope, ax, color=color, smooth=smooth, zoom=zoom)
return ax
def plotLHMRM_index(dataFrame, index, ax, smooth=0):
[fn, pms, preC, fi, proC, isotope] = getInfoMRM(dataFrame, index)
indexOpp = getPairedIonMRM(dataFrame, index)
if isotope == 'heavy':
[index, indexOpp] = [indexOpp, index]
ax = plotMRM_index(dataFrame, index, ax, color='blue', smooth=smooth)
ax = plotMRM_index(dataFrame, indexOpp, ax, color='red', smooth=smooth)
return ax
def plotLHMRM(dataFrame, fileName, pepSeq, precursorCharge, prodCharge, fragIon, ax, smooth=0):
ax = plotMRM(dataFrame, fileName, pepSeq, precursorCharge, prodCharge, fragIon, 'light', ax, color='blue', smooth=smooth)
ax = plotMRM(dataFrame, fileName, pepSeq, precursorCharge, prodCharge, fragIon, 'heavy', ax, color='red', smooth=smooth)
return ax
def prettyPlot3TransMRM(dataFrame, toPlotIndex, figsize=(33,8.5)):
axisArray = []
pylab.figure(figsize=figsize)
for i in range(len(toPlotIndex)):
a = plt.subplot2grid((2,9), (0, i*3), colspan=1, rowspan=2)
a = plotTotalChromPairsMRM(dataFrame, toPlotIndex[i], a, smooth=5, zoom=True)
info = getInfoMRM(dataFrame, toPlotIndex[i])
a.set_title(info[1])
vizLib.tickNum(a, xAxis=4, yAxis=4)
vizLib.cleanAxis(a, ticksOnly=True)
axisArray.append(a)
b = plt.subplot2grid((2,9), (0,i*3+1), colspan=2, rowspan=1)
b = plotAllTransitionsMRM(dataFrame, toPlotIndex[i], b, smooth=5, zoom=True)
info = getInfoMRM(dataFrame, toPlotIndex[i])
b.set_title('light')
vizLib.tickNum(b, xAxis=4, yAxis=4)
vizLib.cleanAxis(b, ticksOnly=True)
axisArray.append(b)
c = plt.subplot2grid((2,9), (1,i*3+1), colspan=2, rowspan=1)
opp = getPairedIonMRM(dataFrame, toPlotIndex[i])
c = plotAllTransitionsMRM(dataFrame, opp, c, smooth=5, zoom=True)
info = getInfoMRM(dataFrame, toPlotIndex[i])
c.set_title('heavy')
vizLib.tickNum(c, xAxis=4, yAxis=4)
vizLib.cleanAxis(c, ticksOnly=True)
axisArray.append(c)
pylab.tight_layout()
return axisArray
def readMRMCSV(path, l = 'light ', h = 'heavy ', fileNameHeader = 'File Name', proteinNameHeader = 'Protein Name'):
fileName = path.split('/')[-1].split('.')
dataFrame = pandas.read_csv(path)
s='_'
dataFrame['shortName'] = dataFrame[fileNameHeader].str.split('.').str[0]
dataFrame['UID'] = dataFrame['shortName'] +s+ dataFrame[proteinNameHeader] +s+ dataFrame['Begin Pos'].map(str) +s+\
dataFrame['End Pos'].map(str) +s+ dataFrame[l + 'Precursor Mz'].map(str).str.split('.').str[0] +s+ \
dataFrame['Product Charge'].map(str) +s+ dataFrame['Fragment Ion'].str[-3:]
dataFrame['TID'] = dataFrame[proteinNameHeader] +s+ dataFrame['Begin Pos'].map(str) +s+\
dataFrame['End Pos'].map(str) +s+ dataFrame[l + 'Precursor Mz'].map(str).str.split('.').str[0] +s+ \
dataFrame['Product Charge'].map(str) +s+ dataFrame['Fragment Ion'].str[-3:]
dataFrame['PID'] = dataFrame['shortName'] +s+ dataFrame[proteinNameHeader] +s+ dataFrame['Begin Pos'].map(str) +s+\
dataFrame['End Pos'].map(str) +s+ dataFrame[l + 'Precursor Mz'].map(str).str.split('.').str[0]
dataFrame[l+'AdjArea'] = dataFrame[l+'Area']
dataFrame[h+'AdjArea'] = dataFrame[h+'Area']
dataFrame['currentCalc'] = calcValue(dataFrame, [l], [h])
dataFrame['ratio'] = calcValue(dataFrame, [l],[h])
dataFrame = dataFrame[pandas.notnull(dataFrame[fileNameHeader])]
positionOtherDict = {key:int(value)+1 for value, key in enumerate(qMS.sort_nicely(sorted(set(dataFrame[proteinNameHeader].values))))}
positionLookupOther = pandas.Series(positionOtherDict)
dataFrame['otherpos']=positionLookupOther[dataFrame[proteinNameHeader]].values
dataFrame['handDelete'] = False
dataFrame['handSave'] = False
dataFrame['PPMtranLH'] = abs(dataFrame[l+'Mass Error PPM'] - dataFrame[h+'Mass Error PPM'])
dataFrame['PPMtranTRANALL_light'] = abs(dataFrame[l+'Mass Error PPM'] - dataFrame[l+'Average Mass Error PPM'])
dataFrame['PPMtranTRANALL_heavy'] = abs(dataFrame[h+'Mass Error PPM'] - dataFrame[h+'Average Mass Error PPM'])
dataFrame['PPMtranTRANALL'] = dataFrame[['PPMtranTRANALL_light', 'PPMtranTRANALL_heavy']].max(axis=1)
dataFrame['RTdsLH'] = abs(dataFrame[l+'Retention Time'] - dataFrame[h+'Retention Time'])
dataFrame['RTdsTRANALL_light'] = abs(dataFrame[l+'Retention Time'] - dataFrame[l+'Best Retention Time'])
dataFrame['RTdsTRANALL_heavy'] = abs(dataFrame[h+'Retention Time'] - dataFrame[h+'Best Retention Time'])
dataFrame['RTdsTRANALL'] = dataFrame[['RTdsTRANALL_light', 'RTdsTRANALL_heavy']].max(axis=1)
dataFrame['RTpepTRANALL_light'] = abs(dataFrame[l+'Retention Time'] - dataFrame['Average Measured Retention Time'])
dataFrame['RTpepTRANALL_heavy'] = abs(dataFrame[h+'Retention Time'] - dataFrame['Average Measured Retention Time'])
dataFrame['RTpepTRANALL'] = dataFrame[['RTpepTRANALL_light', 'RTpepTRANALL_heavy']].max(axis=1)
if not 'priorFilter' in dataFrame.columns:
dataFrame['priorFilter'] = True
if not 'allClear' in dataFrame.columns:
dataFrame['allClear'] = dataFrame['priorFilter']
for p in list(dataFrame[proteinNameHeader].unique()):
peptides = list(dataFrame.loc[dataFrame[proteinNameHeader] == p, 'Peptide Modified Sequence'].unique())
for n, pep in enumerate(peptides):
l = float(len(peptides))
ind = n/l
dataFrame.loc[(dataFrame['Peptide Modified Sequence'] == pep) & (dataFrame[proteinNameHeader] == p),'colOff'] = ind
dataFrame['currentPosDataset']=dataFrame['otherpos']
positionFileDict = {key:int(value)+1 for value,key in enumerate(qMS.sort_nicely(sorted(dataFrame[fileNameHeader].unique())))}
positionFileLookup = pandas.Series(positionFileDict)
dataFrame['currentPosProtein']=positionFileLookup[dataFrame[fileNameHeader]].values
dataFrame.loc[dataFrame['currentCalc'] == numpy.inf, 'allClear'] = False
return dataFrame
def calcValue(df, num, den, field = 'AdjArea', offset=0.0, func=qMS.unity):
nsDF = df[num[0]+field]
for x in num[1:]:
nsDF = nsDF + df[x+field]
if den == 'label-free':
value = nsDF + offset
else:
dsDF = df[den[0]+field]
for x in den[1:]:
dsDF = dsDF + df[x+field]
try:
value = nsDF/dsDF + offset
except TypeError:
print "Error in calculating values - some entry must contain strings."
print "This can be fixed by deleting this row in vi (you'll see a bunch of NaN values there)."
print "Until this is fixed, all values set to 0.0"
value = -10.0
return func(value)
def calcAdjValue(df, isotopeLabels=['light ', 'heavy '], field='Area'):
for i in isotopeLabels:
df.loc[:, i+'AdjArea'] = df[i+field]
return df
def calcMRMTotalProtOcc(dfTotal, proteins, files, num=['light '], den=['heavy ']):
dfTotal.loc[:,'currentCalc'] = calcValue(dfTotal, num, den)
d = {}
for i in proteins:
d[i]={}
for j in files:
peps = list(set(dfTotal[(dfTotal['Protein Name']==i) & (dfTotal['File Name']==j)]['Peptide Modified Sequence']))
d[i][j]=[]
for k in peps:
d[i][j].append(dfTotal[(dfTotal['Protein Name']==i) &
(dfTotal['File Name']==j) &
(dfTotal['Peptide Modified Sequence']==k)]['currentCalc'].values[0])
return d
def calcForUniquePeps(mdf, num = ['light'], den = ['heavy']):
assert len(mdf['File Name'].unique()) == 1, 'Please pass a datafram with only one file name to calcForUniquePeps'
ups = mdf['Peptide Modified Sequence With Charge'].unique()
upsDict = {}
for u in ups:
upsDict[u] = {'precursorCalc':numpy.nan, 'productCalc':numpy.nan, 'totalCalc':numpy.nan}
for pep in ups:
numTotal = [0.0,0.0,0.0]
denTotal = [0.0,0.0,0.0]
for n in num:
numTotal[0] = float(numTotal[0] + mdf.loc[(mdf['Peptide Modified Sequence With Charge'] == pep) &
(mdf['Fragment Ion Type'] == 'precursor'), n+' Area'].sum())
numTotal[1] = float(numTotal[1] + mdf.loc[(mdf['Peptide Modified Sequence With Charge'] == pep) &
(mdf['Fragment Ion Type'] != 'product'), n+' Area'].sum())
numTotal[2] = numTotal[0]+numTotal[1]
for d in den:
denTotal[0] = float(denTotal[0] + mdf.loc[(mdf['Peptide Modified Sequence With Charge'] == pep) &
(mdf['Fragment Ion Type'] == 'precursor'), d+' Area'].sum())
denTotal[1] = float(denTotal[1] + mdf.loc[(mdf['Peptide Modified Sequence With Charge'] == pep) &
(mdf['Fragment Ion Type'] != 'product'), d+' Area'].sum())
denTotal[2] = denTotal[0]+denTotal[1]
try:
upsDict[pep]['precursorCalc'] = numTotal[0]/denTotal[0]
except ZeroDivisionError:
upsDict[pep]['precursorCalc'] = numpy.nan
try:
upsDict[pep]['productCalc'] = numTotal[1]/denTotal[1]
except ZeroDivisionError:
upsDict[pep]['productCalc'] = numpy.nan
try:
upsDict[pep]['totalCalc'] = numTotal[2]/denTotal[2]
except ZeroDivisionError:
upsDict[pep]['totalCalc'] = numpy.nan
return upsDict
def getValByProt(mdf, proteinName, upsDict, averageMethod = 'median', field = 'productCalc', allValues = False):
assert len(mdf['File Name'].unique()) == 1, 'Please pass a dataframe with only a single file name to getValByProt'
assert (averageMethod == 'median' or averageMethod == 'mean')
pepsToAvg = mdf[mdf['Protein Name'] == proteinName]['Peptide Modified Sequence With Charge'].unique()
valList = []
for p in pepsToAvg:
valList.append(upsDict[p][field])
if averageMethod == 'median':
toReturn = numpy.nanmedian(valList)
else:
toReturn = numpy.nanmean(valList)
if allValues:
toReturn = [toReturn]
toReturn.append(valList)
else:
return toReturn
def calcFullOccupancyHeatMap(df, allFiles = None, allProteins = None, num = ['light'], den = ['heavy'], averageMethod = 'median', field = 'productCalc', allValues = False, nameField='File Name'):
if allProteins == None:
allProteins = df['Protein Name'].unique()
if allFiles == None:
allFiles = df[nameField].unique()
prot_file_dict = {}
for p in allProteins:
prot_file_dict[p] = {}
for file_dict in allFiles:
prot_file_dict[p][file_dict] = [numpy.nan]
for f in allFiles:
subDF = df[df[nameField] == f]
upCalc = calcForUniquePeps(subDF)
for p in allProteins:
prot_file_dict[p][f] = getValByProt(subDF, p, upCalc)
prot_file_dataFrame = pandas.DataFrame(prot_file_dict)
return prot_file_dataFrame
def filterDF(df, sigmas=2, ppmDiff=15, RTDiff=None, ldp=0.2, lhDP=0.6, nameField='File Name'):
df['Peptide Modified Sequence With Charge'] = df['Peptide Modified Sequence'] + '[+' + df['Precursor Charge'].map(str) + ']'
fileNames = qMS.sort_nicely(list(df[nameField].unique()))
for fn in fileNames:
uH = df[df[nameField] == fn]['heavy Mass Error PPM'].mean()
uL = df[df[nameField] == fn]['light Mass Error PPM'].mean()
sH = df[df[nameField] == fn]['heavy Mass Error PPM'].std()
sL = df[df[nameField] == fn]['light Mass Error PPM'].std()
df.loc[(df[nameField] == fn), 'hPPM'] = abs(df.loc[(df[nameField] == fn), 'heavy Mass Error PPM'] - uH) < sigmas * sH
df.loc[(df[nameField] == fn), 'lPPM'] = abs(df.loc[(df[nameField] == fn), 'light Mass Error PPM'] - uL) < sigmas * sL
df.loc[(df[nameField] == fn), 'ppmDiff'] = abs(df.loc[(df[nameField] == fn), 'heavy Mass Error PPM'] -
df.loc[(df[nameField] == fn), 'light Mass Error PPM']) < ppmDiff
if not RTDiff is None:
df.loc[(df[nameField] == fn), 'RTDiff'] = abs(df.loc[(df[nameField] == fn), 'light Retention Time'] -
df.loc[(df[nameField] == fn), 'heavy Retention Time']) < RTDiff
df.loc[(df[nameField] == fn), 'hLDP'] = df.loc[(df[nameField] == fn), 'heavy Library Dot Product'] > ldp
df.loc[(df[nameField] == fn), 'lLDP'] = df.loc[(df[nameField] == fn), 'light Library Dot Product'] > ldp
df.loc[(df[nameField] == fn), 'dpLTH'] = df.loc[(df[nameField] == fn), 'DotProductLightToHeavy'] > lhDP
df.loc[(df[nameField] == fn), 'allClear'] = ((df.loc[(df[nameField] == fn), 'hPPM']) &
(df.loc[(df[nameField] == fn), 'lPPM']) &
(df.loc[(df[nameField] == fn), 'ppmDiff']) &
(df.loc[(df[nameField] == fn), 'RTDiff']) &
(df.loc[(df[nameField] == fn), 'hLDP']) &
(df.loc[(df[nameField] == fn), 'lLDP']) &
(df.loc[(df[nameField] == fn), 'dpLTH']))
return df