def mixBispecYN(L0, KxStar, Rtot, Kav, fully=True):
""" Compare bispecific to mixture of bivalent """
if fully:
return polyc(L0, KxStar, Rtot, [[1, 1]], [1.0], Kav)[2][0] / \
np.sum(polyc(L0, KxStar, Rtot, [[2, 0], [0, 2]], [0.5, 0.5], Kav)[2])
else:
return polyc(L0, KxStar, Rtot, [[1, 1]], [1.0], Kav)[0][0] / \
np.sum(polyc(L0, KxStar, Rtot, [[2, 0], [0, 2]], [0.5, 0.5], Kav)[0])
def tetheredYN(L0, KxStar, Rtot, Kav, fully=True):
""" Compare tethered (bispecific) vs monovalent """
if fully:
return polyc(L0, KxStar, Rtot, [[1, 1]], [1.0], Kav)[2][0] / \
polyfc(L0 * 2, KxStar, 1, Rtot, [0.5, 0.5], Kav)[0]
else:
return polyc(L0, KxStar, Rtot, [[1, 1]], [1.0], Kav)[0][0] / \
polyfc(L0 * 2, KxStar, 1, Rtot, [0.5, 0.5], Kav)[0]
def selectivity(pop1name, pop2name, L0, KxStar, Cplx, Ctheta, Kav, fully=True, untethered=False):
""" Always calculate the full binding of the 1st kind of complex """
pop1 = cellPopulations[pop1name][0], cellPopulations[pop1name][1]
pop2 = cellPopulations[pop2name][0], cellPopulations[pop2name][1]
if untethered: # mixture of monovalent
return polyfc(L0, KxStar, 1, np.power(10, pop1), [0.5, 0.5], Kav)[0] \
/ polyfc(L0, KxStar, 1, np.power(10, pop2), [0.5, 0.5], Kav)[0]
if fully:
return np.sum(polyc(L0, KxStar, np.power(10, pop1), Cplx, Ctheta, Kav)[2]) \
/ np.sum(polyc(L0, KxStar, np.power(10, pop2), Cplx, Ctheta, Kav)[2])
else:
return np.sum(polyc(L0, KxStar, np.power(10, pop1), Cplx, Ctheta, Kav)[0]) \
/ np.sum(polyc(L0, KxStar, np.power(10, pop2), Cplx, Ctheta, Kav)[0])
def heatmapNorm(ax, R0, L0, KxStar, Kav, Comp, f=None, Cplx=None, vrange=(0, 5), title="", cbar=False, layover=2, highlight=[], lineN=101, recFactor=1.0):
assert bool(f is None) != bool(Cplx is None)
nAbdPts = 70
abundRange = (LR + np.log10(recFactor), HR + np.log10(recFactor))
abundScan = np.logspace(abundRange[0], abundRange[1], nAbdPts)
if f is None:
func = np.vectorize(lambda abund1, abund2: polyc(L0, KxStar, [abund1, abund2], Cplx, Comp, Kav)[2][0])
else:
func = np.vectorize(lambda abund1, abund2: polyfc(L0, KxStar, f, [abund1, abund2], Comp, Kav)[0])
func0 = func(10**(R0[0] + np.log10(recFactor)), 10**(R0[1] + np.log10(recFactor)))
X, Y = np.meshgrid(abundScan, abundScan)
Z = func(X, Y) / func0
contours1 = ax.contour(X, Y, Z, levels=np.logspace(0, 10, (lineN - 1) // 2 + 1)[1:], colors="black", linewidths=0.5)
contours0 = ax.contour(X, Y, Z, levels=np.logspace(-10, 0, (lineN - 1) // 2 + 1), colors="white", linewidths=0.5)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(title)
plt.clabel(contours1, inline=True, fontsize=8, fmt="%3.1g")
plt.clabel(contours0, inline=True, fontsize=8, fmt="%3.1g")
ax.pcolor(X, Y, Z, cmap='viridis', vmin=vrange[0], vmax=vrange[1])
norm = plt.Normalize(vmin=vrange[0], vmax=vrange[1])
if cbar:
cbar = ax.figure.colorbar(cm.ScalarMappable(norm=norm, cmap='viridis'), ax=ax)
cbar.set_label("Relative Ligand Bound")
# layover: 2 = with name; 1 = only pop w/o name; 0 = none
if layover == 2:
overlapCellPopulation(ax, abundRange, highlight=highlight, recFactor=np.log10(recFactor), pname=True)
elif layover == 1:
overlapCellPopulation(ax, abundRange, highlight=highlight, recFactor=np.log10(recFactor), pname=False)
def heatmap(ax, L0, KxStar, Kav, Comp, f=None, Cplx=None, vrange=(-2, 4), title="", cbar=False, layover=2, fully=False, highlight=[]):
assert bool(f is None) != bool(Cplx is None)
nAbdPts = 70
abundRange = (LR, HR)
abundScan = np.logspace(abundRange[0], abundRange[1], nAbdPts)
if f is None:
if fully:
func = np.vectorize(lambda abund1, abund2: np.sum(polyc(L0, KxStar, [abund1, abund2], Cplx, Comp, Kav)[2]))
else:
func = np.vectorize(lambda abund1, abund2: np.sum(polyc(L0, KxStar, [abund1, abund2], Cplx, Comp, Kav)[0]))
else:
func = np.vectorize(lambda abund1, abund2: polyfc(L0, KxStar, f, [abund1, abund2], Comp, Kav)[0])
X, Y = np.meshgrid(abundScan, abundScan)
logZ = np.log(func(X, Y))
vmed = int((vrange[0] + vrange[1]) / 2)
contours0 = ax.contour(X, Y, logZ, levels=np.arange(-20, vmed, 1), colors="white", linewidths=0.5)
contours1 = ax.contour(X, Y, logZ, levels=np.arange(vmed, 20, 1), colors="black", linewidths=0.5)
ax.set_xscale("log")
ax.set_yscale("log")
ax.yaxis.set_major_formatter(mticker.ScalarFormatter(useOffset=False, useMathText=True))
ax.set_title(title)
plt.clabel(contours0, inline=True, fontsize=8)
plt.clabel(contours1, inline=True, fontsize=8)
ax.pcolor(X, Y, logZ, cmap='viridis', vmin=vrange[0], vmax=vrange[1])
norm = plt.Normalize(vmin=vrange[0], vmax=vrange[1])
if cbar:
cbar = ax.figure.colorbar(cm.ScalarMappable(norm=norm, cmap='viridis'), ax=ax)
cbar.set_label("Log Ligand Bound")
# layover: 2 = with name; 1 = only pop w/o name; 0 = none
if layover == 2:
overlapCellPopulation(ax, abundRange, highlight=highlight, pname=True)
elif layover == 1:
overlapCellPopulation(ax, abundRange, highlight=highlight, pname=False)
def sigmaPopC(name, L0, KxStar, Cplx, Ctheta, Kav, quantity=0, h=None):
return np.array([
polyc(L0, KxStar, Rtot, Cplx, Ctheta, Kav)[quantity][1]
for Rtot in sigmapts(name, h=h)
]).reshape(-1)
def heatmapDL(ax,
L0,
KxStar,
Kav,
Comp,
Cplx=None,
vrange=(-2, 4),
title="",
cbar=True,
dead=False,
jTherap=False,
highlight=[],
lowlight=[]):
"Makes a heatmap with modified cell population abundances according to Antagonist binding"
nAbdPts = 70
abunds = np.array(list(cellPopulations.values()))[:, 0:2]
abundRange = (np.amin(abunds) - 1, np.amax(abunds) + 1)
abundScan = np.logspace(abundRange[0], abundRange[1], nAbdPts)
if dead:
func = np.vectorize(lambda abund1, abund2: polyc(
L0, KxStar, [abund1, abund2], Cplx, Comp, Kav)[0][0])
title = "Antagonist Bound"
else:
func = np.vectorize(lambda abund1, abund2: polyc(
L0, KxStar, [abund1, abund2], Cplx, Comp, Kav)[0][1])
if jTherap:
title = "Agonist Bound without Antagonist"
else:
title = "Agonist Bound with Antagonist"
X, Y = np.meshgrid(abundScan, abundScan)
logZ = np.log(func(X, Y))
vmed = int((vrange[0] + vrange[1]) / 2)
contours0 = ax.contour(X,
Y,
logZ,
levels=np.arange(-20, vmed, 1.0),
colors="white",
linewidths=0.5)
contours1 = ax.contour(X,
Y,
logZ,
levels=np.arange(vmed, 20, 1.0),
colors="black",
linewidths=0.5)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(title)
plt.clabel(contours0, inline=True, fontsize=8)
plt.clabel(contours1, inline=True, fontsize=8)
ax.pcolor(X, Y, logZ, cmap='viridis', vmin=vrange[0], vmax=vrange[1])
norm = plt.Normalize(vmin=vrange[0], vmax=vrange[1])
ax.set(xlabel="Receptor 1 Abundance (#/cell)",
ylabel='Receptor 2 Abundance (#/cell)')
if cbar:
cbar = ax.figure.colorbar(cm.ScalarMappable(norm=norm, cmap='viridis'),
ax=ax,
aspect=40)
cbar.set_label("Log Ligand Bound")
overlapCellPopulation(ax,
abundRange,
data=cellPopulations,
highlight=highlight,
lowlight=lowlight)