def breslauerMethod(self,E=None, d=None, xy=None,invert=False,
show=True,figname=None):
"""Finds slope of trans region and plugs this in to equation
http://www.springerlink.com/content/r34n0201g30563u7/ """
if E !=None:
ek = E.getDataset(d)
x,y,a, xerr,yerr = ek.getAll()
elif xy!=None:
x,y = xy
else:
return
f=plt.figure(figsize=(10,6))
ax=f.add_subplot(111)
ax.set_xlabel('T')
p=ax.plot(x,y,'o',alpha=0.5)
d50 = self.guessMidpoint(x,y)
A,X=Fitting.doFit(expdata=zip(x,y),model='Unfolding',conv=1e-7,noiter=60,
guess=False,startvalues=[1,1,1,1,1,d50])
fity = X.getFitLine(x)
p=ax.plot(x,fity,'r',lw=2)
fd=X.getFitDict()
self.drawParams(ax,fd)
Tm = fd['d50']; m = fd['m']
R = 8.3144e-3
deltaH = R * math.pow(Tm,2) * m
f.suptitle("Method 4 - deltaH: %2.2f Tm: %2.2f" %(deltaH,Tm),size=18)
if show == True:
self.showTkFigure(f)
if figname != None:
figname = figname.replace('.','_')
f.savefig(figname)
plt.close()
return deltaH, Tm
def fitElwellSchellman(self,E=None, d=None, xy=None,transwidth=50,
invert=False,show=True,figname=None):
"""Fit entire raw data simultaneously to the three main thermodynamic
parameters using Elwell/Schellman method"""
if E !=None:
ek = E.getDataset(d)
x,y,a, xerr,yerr = ek.getAll()
elif xy!=None:
x,y = xy
else:
return
if invert == True:
y = [max(y)-i for i in y[:]]
f=plt.figure(figsize=(10,5))
ax=f.add_subplot(121)
p=ax.plot(x,y,'o',alpha=0.5)
ax.set_xlabel('T');ax.set_xlabel('mdeg')
ax.set_title('raw data')
x1,y1,x,y = self.transformCD(x,y,transwidth,ax)
t=[];dg=[]
R=8.3144e-3
for T,fu in zip(x,y):
if fu>=1 or fu<=0:
continue
K = fu/(1-fu)
deltaGt = -R * T * math.log(K)
dg.append(deltaGt)
t.append(T)
ax1=f.add_subplot(122)
p=ax1.plot(t,dg,'x',mew=2,color='black')
ax1.set_xlabel('T'); ax1.set_ylabel('dG(T)')
ax.set_title('stability curve')
A,X=Fitting.doFit(expdata=zip(t,dg),model='schellman',grad=1e-9,conv=1e-9)
fity = X.getFitLine(t)
p=ax1.plot(t,fity,'r',lw=2)
fd=X.getFitDict()
self.drawParams(ax1,fd)
deltaH=fd['deltaH']; deltacp=fd['deltacp']; Tm=fd['Tm']
f.suptitle("Method 2 - deltaH: %2.2f deltaCp: %2.2e Tm: %2.2f" %(deltaH,deltacp,Tm),size=18)
if show == True:
self.showTkFigure(f)
if figname == None: figname = d
figname = figname.replace('.','_')
fname = figname+'m1'+'.png'
f.savefig(fname,dpi=300)
print 'plot saved to %s' %os.path.abspath(fname)
if E!=None:
fdata = Fitting.makeFitData(X.name,vrs=X.variables)
E.insertDataset(xydata=[t,dg], newname=d+'_vanthoff2',replace=True,fit=fdata)
#E.saveProject()
return deltaH, Tm, deltacp
def transformCD(self, x, y, transwidth=None, ax=None):
"""Transform raw data into fraction unfolded per temp value, by fitting to
a general unfolding equation that extracts baseline/slopes"""
#fit baseline slopes and get intercepts
d50 = self.guessMidpoint(x, y)
print 'fitting to get baseline slopes and intercepts..'
print 'midpoint is %s' % d50
A, X = Fitting.doFit(expdata=zip(x, y),
model='Unfolding',
noiter=50,
silent=True,
guess=False,
startvalues=[1, 1, 1, 1, 1, d50])
#print X.getResult()
fity = X.getFitLine(x)
fd = X.getFitDict()
if ax != None:
p = ax.plot(x, fity, 'r', lw=2)
self.drawParams(ax, fd)
#we then use slopes and intercepts get frac unfolded at each temp
mn = fd['bn']
mu = fd['bd'] #slopes
#if mu>0.01: mu = 0.01
yn = fd['an']
yu = fd['ad'] #intercepts
d50 = fd['d50']
m = fd['m']
t = []
f = []
#print mu, mn
for T, yo in zip(x, y):
fu = (yo - (yn + mn * T)) / ((yu + mu * T) - (yn + mn * T))
#print fu, (yo-(yn+mn*T)), (m), mu, mn
#if f>0:
f.append(fu)
t.append(T)
#try to take useful transition region of data
at, af = t, f
diff = 1e5
if transwidth != None:
for i in t:
d = abs(i - d50)
if d < diff:
mid = t.index(i)
diff = d
L = int(mid - transwidth)
U = int(mid + transwidth)
t, f = t[L:U], f[L:U]
return at, af, t, f
def breslauerMethod(self,
E=None,
d=None,
xy=None,
invert=False,
show=True,
figname=None):
"""Finds slope of trans region and plugs this in to equation
http://www.springerlink.com/content/r34n0201g30563u7/ """
if E != None:
ek = E.getDataset(d)
x, y, a, xerr, yerr = ek.getAll()
elif xy != None:
x, y = xy
else:
return
f = plt.figure(figsize=(10, 6))
ax = f.add_subplot(111)
ax.set_xlabel('T')
p = ax.plot(x, y, 'o', alpha=0.5)
d50 = self.guessMidpoint(x, y)
A, X = Fitting.doFit(expdata=zip(x, y),
model='Unfolding',
conv=1e-7,
noiter=60,
guess=False,
startvalues=[1, 1, 1, 1, 1, d50])
fity = X.getFitLine(x)
p = ax.plot(x, fity, 'r', lw=2)
fd = X.getFitDict()
self.drawParams(ax, fd)
Tm = fd['d50']
m = fd['m']
R = 8.3144e-3
deltaH = R * math.pow(Tm, 2) * m
f.suptitle("Method 4 - deltaH: %2.2f Tm: %2.2f" % (deltaH, Tm),
size=18)
if show == True:
self.showTkFigure(f)
if figname != None:
figname = figname.replace('.', '_')
f.savefig(figname)
plt.close()
return deltaH, Tm
def transformCD(self,x,y,transwidth=None,ax=None):
"""Transform raw data into fraction unfolded per temp value, by fitting to
a general unfolding equation that extracts baseline/slopes"""
#fit baseline slopes and get intercepts
d50 = self.guessMidpoint(x,y)
print 'fitting to get baseline slopes and intercepts..'
print 'midpoint is %s' %d50
A,X=Fitting.doFit(expdata=zip(x,y),model='Unfolding',noiter=50,silent=True,
guess=False,startvalues=[1,1,1,1,1,d50])
#print X.getResult()
fity = X.getFitLine(x)
fd=X.getFitDict()
if ax!=None:
p=ax.plot(x,fity,'r',lw=2)
self.drawParams(ax,fd)
#we then use slopes and intercepts get frac unfolded at each temp
mn = fd['bn']; mu = fd['bd'] #slopes
#if mu>0.01: mu = 0.01
yn = fd['an']; yu = fd['ad'] #intercepts
d50 = fd['d50']; m = fd['m']
t=[]; f=[]
#print mu, mn
for T,yo in zip(x,y):
fu = (yo-(yn+mn*T)) / ((yu+mu*T)-(yn+mn*T))
#print fu, (yo-(yn+mn*T)), (m), mu, mn
#if f>0:
f.append(fu)
t.append(T)
#try to take useful transition region of data
at,af=t,f
diff=1e5
if transwidth != None:
for i in t:
d=abs(i-d50)
if d<diff:
mid = t.index(i)
diff=d
L=int(mid-transwidth); U=int(mid+transwidth)
t,f = t[L:U], f[L:U]
return at,af,t,f
def fitDifferentialCurve(self, E=None, d=None, xy=None,smooth=0,
invert=False,show=True,figname=None):
"""Derive differential denaturation curve and fit to get deltaH
We smooth the unfolding curve and then differentiate and finally
fit to a 3 parameter equation.
See http://www.ncbi.nlm.nih.gov/pubmed/10933511"""
if E !=None:
ek = E.getDataset(d)
x,y,a, xerr,yerr = ek.getAll()
elif xy!=None:
x,y = xy
else:
return
if invert == True:
y = [max(y)-i for i in y[:]]
leg=[]; lines=[]
f=plt.figure(figsize=(10,5))
ax=f.add_subplot(121)
p=ax.plot(x,y,'x',color='black',mew=3,alpha=0.5)
leg.append(p); lines.append('original')
#smooth
if smooth == 0:
smooth=int(len(x)/15.0)
s=self.smoothListGaussian(y,smooth)
p=ax.plot(x[:len(s)-1],s[:-1],lw=3)
leg.append(p); lines.append('smoothed')
ax.set_title("original data")
ax.set_xlabel('T')
ax1=f.add_subplot(122)
#differentiate
dx,ds = self.differentiate(x[:len(s)],s)
#ds = [i/max(ds) for i in ds]
ds = [i*10 for i in ds]
cw=csv.writer(open('diffcd.csv','w'))
for row in zip(dx,ds):
cw.writerow(row)
p=ax1.plot(dx,ds,'-',lw=1.5,alpha=0.7,color='black')
leg.append(p); lines.append('differential')
ax1.set_title("differential denaturation")
ax1.set_xlabel('T'); ax1.set_ylabel('dsignal/dT')
A,X=Fitting.doFit(expdata=zip(dx,ds),model='diffDenaturation',grad=1e-9,conv=1e-10)
fity = X.getFitLine(dx)
p=ax1.plot(dx,fity,'r',lw=2)
leg.append(p); lines.append('fit')
t=X.getFitDict()
self.drawParams(ax1,t)
dHkcal=t['deltaH']/4.184
f.suptitle('Method 3 - deltaH: %2.2f kJ/mol (%2.2f kcal) Tm: %2.2f' %(t['deltaH'],dHkcal,t['Tm']),size=18)
ax.legend(leg,lines,loc='best',prop=FontProperties(size="smaller"))
#f.subplots_adjust(hspace=0.8)
if show == True:
self.showTkFigure(f)
if figname != None:
figname = figname.replace('.','_')
f.savefig(figname+'m3',dpi=300)
plt.close()
if E!=None:
fdata = Fitting.makeFitData(X.name,vrs=X.variables)
E.insertDataset(xydata=[dx,ds], newname=d+'_diff',replace=True,fit=fdata)
#E.saveProject()
return t['deltaH'],t['Tm']
def fitVantHoff(self, E=None, d=None, xy=None, transwidth=80, invert=False,
show=True, figname=None):
"""Derive fraction unfolded, get K and fit to Van't Hoff.
see http://www.jbc.org/content/277/43/40717.full
or http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2144003/
"""
if E != None:
if not d in E.datasets:
print 'no such dataset, %s' %d
print 'available datasets:', E.datasets
return
ek = E.getDataset(d)
x,y = ek.getxySorted()
elif xy!=None:
x,y = xy
if 'Convert Celsius-Kelvin' in self.conversions.getcurselection():
x = [i+273 for i in x]
if invert == True:
y = [max(y)-i for i in y[:]]
f=plt.figure(figsize=(18,6))
ax=f.add_subplot(131)
p=ax.plot(x,y,'o',alpha=0.6)
ax.set_xlabel('T(K)'); ax.set_ylabel('mdeg')
ax.set_title('raw data')
x1,y1,x,y = self.transformCD(x,y,transwidth,ax)
cw=csv.writer(open('frac_unfolded_'+d+'.csv','w'))
cw.writerow(['temp','frac'])
for i in zip(x1,y1):
cw.writerow(i)
#derive lnK vs 1/T
t=[]; k=[]
for T,fu in zip(x,y):
if fu>=1 or fu<=0:
continue
K = fu/(1-fu)
klog = math.log(K)
k.append(klog)
t.append(1/T)
if len(t)<2: return None, None, None
ax=f.add_subplot(132)
p=ax.plot(x1,y1,'o',color='g',alpha=0.6)
ax.set_xlabel('T(K)'); ax.set_ylabel('fu')
ax.set_title('fraction unfolded')
ax=f.add_subplot(133)
p=ax.plot(t,k,'x',mew=2,color='black')
ax.set_xlabel('1/T')#(r'$1/T ($K^-1)$')
ax.set_ylabel('ln K')
formatter = matplotlib.ticker.ScalarFormatter()
formatter.set_scientific(True)
formatter.set_powerlimits((0,0))
ax.xaxis.set_major_formatter(formatter)
for l in ax.get_xticklabels():
l.set_rotation(30)
#fit this van't hoff plot
A,X=Fitting.doFit(expdata=zip(t,k),model='Linear')
fitk = X.getFitLine(t)
p=ax.plot(t,fitk,'r',lw=2)
fd=X.getFitDict()
#self.drawParams(ax,fd)
#slope is deltaH/R/1000 in kJ
deltaH = -fd['a']*self.R/1000
deltaS = fd['b']*self.R/1000
f.suptitle("Method 1 - deltaH: %2.2f deltaS: %2.2f" %(deltaH,deltaS),size=18)
f.subplots_adjust(bottom=0.15,top=0.85)
if show==True:
self.showTkFigure(f)
if figname == None: figname = d
figname = figname.replace('.','_')
fname = figname+'m1'+'.png'
f.savefig(fname,dpi=300)
print 'plot saved to %s' %os.path.abspath(fname)
#plt.close()
if E!=None:
fdata = Fitting.makeFitData(X.name,vrs=X.variables)
E.insertDataset(xydata=[t,k], newname=d+'_vanthoff',replace=True,fit=fdata)
#E.saveProject()
return deltaH, deltaS, ax
def fitDifferentialCurve(self,
E=None,
d=None,
xy=None,
smooth=0,
invert=False,
show=True,
figname=None):
"""Derive differential denaturation curve and fit to get deltaH
We smooth the unfolding curve and then differentiate and finally
fit to a 3 parameter equation.
See http://www.ncbi.nlm.nih.gov/pubmed/10933511"""
if E != None:
ek = E.getDataset(d)
x, y, a, xerr, yerr = ek.getAll()
elif xy != None:
x, y = xy
else:
return
if invert == True:
y = [max(y) - i for i in y[:]]
leg = []
lines = []
f = plt.figure(figsize=(10, 5))
ax = f.add_subplot(121)
p = ax.plot(x, y, 'x', color='black', mew=3, alpha=0.5)
leg.append(p)
lines.append('original')
#smooth
if smooth == 0:
smooth = int(len(x) / 15.0)
s = self.smoothListGaussian(y, smooth)
p = ax.plot(x[:len(s) - 1], s[:-1], lw=3)
leg.append(p)
lines.append('smoothed')
ax.set_title("original data")
ax.set_xlabel('T')
ax1 = f.add_subplot(122)
#differentiate
dx, ds = self.differentiate(x[:len(s)], s)
#ds = [i/max(ds) for i in ds]
ds = [i * 10 for i in ds]
cw = csv.writer(open('diffcd.csv', 'w'))
for row in zip(dx, ds):
cw.writerow(row)
p = ax1.plot(dx, ds, '-', lw=1.5, alpha=0.7, color='black')
leg.append(p)
lines.append('differential')
ax1.set_title("differential denaturation")
ax1.set_xlabel('T')
ax1.set_ylabel('dsignal/dT')
A, X = Fitting.doFit(expdata=zip(dx, ds),
model='diffDenaturation',
grad=1e-9,
conv=1e-10)
fity = X.getFitLine(dx)
p = ax1.plot(dx, fity, 'r', lw=2)
leg.append(p)
lines.append('fit')
t = X.getFitDict()
self.drawParams(ax1, t)
dHkcal = t['deltaH'] / 4.184
f.suptitle('Method 3 - deltaH: %2.2f kJ/mol (%2.2f kcal) Tm: %2.2f' %
(t['deltaH'], dHkcal, t['Tm']),
size=18)
ax.legend(leg, lines, loc='best', prop=FontProperties(size="smaller"))
#f.subplots_adjust(hspace=0.8)
if show == True:
self.showTkFigure(f)
if figname != None:
figname = figname.replace('.', '_')
f.savefig(figname + 'm3', dpi=300)
plt.close()
if E != None:
fdata = Fitting.makeFitData(X.name, vrs=X.variables)
E.insertDataset(xydata=[dx, ds],
newname=d + '_diff',
replace=True,
fit=fdata)
#E.saveProject()
return t['deltaH'], t['Tm']
def fitElwellSchellman(self,
E=None,
d=None,
xy=None,
transwidth=50,
invert=False,
show=True,
figname=None):
"""Fit entire raw data simultaneously to the three main thermodynamic
parameters using Elwell/Schellman method"""
if E != None:
ek = E.getDataset(d)
x, y, a, xerr, yerr = ek.getAll()
elif xy != None:
x, y = xy
else:
return
if invert == True:
y = [max(y) - i for i in y[:]]
f = plt.figure(figsize=(10, 5))
ax = f.add_subplot(121)
p = ax.plot(x, y, 'o', alpha=0.5)
ax.set_xlabel('T')
ax.set_xlabel('mdeg')
ax.set_title('raw data')
x1, y1, x, y = self.transformCD(x, y, transwidth, ax)
t = []
dg = []
R = 8.3144e-3
for T, fu in zip(x, y):
if fu >= 1 or fu <= 0:
continue
K = fu / (1 - fu)
deltaGt = -R * T * math.log(K)
dg.append(deltaGt)
t.append(T)
ax1 = f.add_subplot(122)
p = ax1.plot(t, dg, 'x', mew=2, color='black')
ax1.set_xlabel('T')
ax1.set_ylabel('dG(T)')
ax.set_title('stability curve')
A, X = Fitting.doFit(expdata=zip(t, dg),
model='schellman',
grad=1e-9,
conv=1e-9)
fity = X.getFitLine(t)
p = ax1.plot(t, fity, 'r', lw=2)
fd = X.getFitDict()
self.drawParams(ax1, fd)
deltaH = fd['deltaH']
deltacp = fd['deltacp']
Tm = fd['Tm']
f.suptitle("Method 2 - deltaH: %2.2f deltaCp: %2.2e Tm: %2.2f" %
(deltaH, deltacp, Tm),
size=18)
if show == True:
self.showTkFigure(f)
if figname == None: figname = d
figname = figname.replace('.', '_')
fname = figname + 'm1' + '.png'
f.savefig(fname, dpi=300)
print 'plot saved to %s' % os.path.abspath(fname)
if E != None:
fdata = Fitting.makeFitData(X.name, vrs=X.variables)
E.insertDataset(xydata=[t, dg],
newname=d + '_vanthoff2',
replace=True,
fit=fdata)
#E.saveProject()
return deltaH, Tm, deltacp
def fitVantHoff(self,
E=None,
d=None,
xy=None,
transwidth=80,
invert=False,
show=True,
figname=None):
"""Derive fraction unfolded, get K and fit to Van't Hoff.
see http://www.jbc.org/content/277/43/40717.full
or http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2144003/
"""
if E != None:
if not d in E.datasets:
print 'no such dataset, %s' % d
print 'available datasets:', E.datasets
return
ek = E.getDataset(d)
x, y = ek.getxySorted()
elif xy != None:
x, y = xy
if 'Convert Celsius-Kelvin' in self.conversions.getcurselection():
x = [i + 273 for i in x]
if invert == True:
y = [max(y) - i for i in y[:]]
f = plt.figure(figsize=(18, 6))
ax = f.add_subplot(131)
p = ax.plot(x, y, 'o', alpha=0.6)
ax.set_xlabel('T(K)')
ax.set_ylabel('mdeg')
ax.set_title('raw data')
x1, y1, x, y = self.transformCD(x, y, transwidth, ax)
cw = csv.writer(open('frac_unfolded_' + d + '.csv', 'w'))
cw.writerow(['temp', 'frac'])
for i in zip(x1, y1):
cw.writerow(i)
#derive lnK vs 1/T
t = []
k = []
for T, fu in zip(x, y):
if fu >= 1 or fu <= 0:
continue
K = fu / (1 - fu)
klog = math.log(K)
k.append(klog)
t.append(1 / T)
if len(t) < 2: return None, None, None
ax = f.add_subplot(132)
p = ax.plot(x1, y1, 'o', color='g', alpha=0.6)
ax.set_xlabel('T(K)')
ax.set_ylabel('fu')
ax.set_title('fraction unfolded')
ax = f.add_subplot(133)
p = ax.plot(t, k, 'x', mew=2, color='black')
ax.set_xlabel('1/T') #(r'$1/T ($K^-1)$')
ax.set_ylabel('ln K')
formatter = matplotlib.ticker.ScalarFormatter()
formatter.set_scientific(True)
formatter.set_powerlimits((0, 0))
ax.xaxis.set_major_formatter(formatter)
for l in ax.get_xticklabels():
l.set_rotation(30)
#fit this van't hoff plot
A, X = Fitting.doFit(expdata=zip(t, k), model='Linear')
fitk = X.getFitLine(t)
p = ax.plot(t, fitk, 'r', lw=2)
fd = X.getFitDict()
#self.drawParams(ax,fd)
#slope is deltaH/R/1000 in kJ
deltaH = -fd['a'] * self.R / 1000
deltaS = fd['b'] * self.R / 1000
f.suptitle("Method 1 - deltaH: %2.2f deltaS: %2.2f" % (deltaH, deltaS),
size=18)
f.subplots_adjust(bottom=0.15, top=0.85)
if show == True:
self.showTkFigure(f)
if figname == None: figname = d
figname = figname.replace('.', '_')
fname = figname + 'm1' + '.png'
f.savefig(fname, dpi=300)
print 'plot saved to %s' % os.path.abspath(fname)
#plt.close()
if E != None:
fdata = Fitting.makeFitData(X.name, vrs=X.variables)
E.insertDataset(xydata=[t, k],
newname=d + '_vanthoff',
replace=True,
fit=fdata)
#E.saveProject()
return deltaH, deltaS, ax
