def form_gate(gate):
"""Deconvolutes string flow gate object"""
vertices = ast.literal_eval(
textwrap.dedent(str(gate).split("Vertices: ")[1].split("Channel")[0]))
channels = ast.literal_eval(
textwrap.dedent(str(gate).split("Channel(s): ")[1].split("Name")[0]))
return PolyGate(vertices, channels)
def makeGate(lowerCorner, upperCorner, channels, name):
"""Returns square gate using upper and lower corners"""
return PolyGate([(lowerCorner[0], lowerCorner[1]),
(upperCorner[0], lowerCorner[1]),
(upperCorner[0], upperCorner[1]),
(lowerCorner[0], upperCorner[1])],
channels,
region='in',
name=name)
import matplotlib.pyplot as plt
#%%
datadir = '/Users/oyleryaniva/Documents/Python/Comps'
pattern = 'Compensation'
Comps = LFS.FlowData(datadir, pattern)
#Comps = Comps.asinhtform(Comps.fluorescent_channel_names,150)
#%%
Comps.samples[0].view_interactively()
#%% general gates
from FlowCytometryTools import PolyGate
gate1 = PolyGate([(3.030e+04, 6.309e+04), (3.266e+04, 2.436e+04),
(4.636e+04, 1.281e+04), (1.531e+05, 1.893e+04),
(2.609e+05, 1.147e+05), (2.481e+05, 2.248e+05),
(1.834e+05, 2.445e+05), (9.597e+04, 2.105e+05)],
('FSC-A', 'SSC-A'),
region='in',
name='gate1')
gate2 = PolyGate([(2.102e+04, 7.858e+04), (1.157e+05, 8.865e+04),
(1.477e+05, 7.507e+04), (1.414e+05, 6.456e+04),
(1.884e+04, 6.368e+04), (1.884e+04, 6.368e+04)],
('SSC-H', 'SSC-W'),
region='in',
name='gate2')
Comps = Comps.gate(gate1).gate(gate2)
#Comps1 = Comps1.gate(gate1)
#%% Gating individual samples
from FlowCytometryTools import ThresholdGate
def rePlot(self,UIIndex,fileIndex):
#Clear plot and replot
plot = self.UIDic["Plots"][UIIndex]
plot.clear()
xSelect = self.UIDic["XAxisSelectors"][UIIndex]
ySelect = self.UIDic["YAxisSelectors"][UIIndex]
#Check if we are gating and if yes gate
gating = False
for checkBox in self.UIDic["GateCheckBoxes"]:
if checkBox.isChecked():
gating=True
gateIndex = self.UIDic["GateCheckBoxes"].index(checkBox)
break
if gating:
#Get coords of
data = self.dataDic["Data"][fileIndex]
roi = self.UIDic["ROIs"][gateIndex]
gateBounds = roi.parentBounds()
x1 = gateBounds.bottomLeft().x()
x2 = gateBounds.bottomRight().x()
y1 = gateBounds.topLeft().y()
y2 = gateBounds.bottomRight().y()
xChannel = str(self.UIDic["XAxisSelectors"][gateIndex].currentText())
yChannel = str(self.UIDic["YAxisSelectors"][gateIndex].currentText())
gate = PolyGate([(x1,y1),(x2,y1),(x2,y2),(x1,y2)],channels = [xChannel,yChannel])
data = data.gate(gate)
else:
data = self.dataDic["Data"][fileIndex]
if str(ySelect.currentText()) == "Events":
z = data[str(xSelect.currentText())]
if len(z) > 0:
y,x = np.histogram(z, bins=np.linspace(min(z), max(z), max(z)/500))
curve = pg.PlotCurveItem(x, y, stepMode=True, fillLevel=0, brush=(0, 0, 255, 80))
for reigon in self.UIDic["AverageReigons"][UIIndex]:
x1,x3
else:
curve = pg.PlotCurveItem([], [])
else:
curve = pg.ScatterPlotItem(pen=None,brush=(1,2),pxMode=True,size=2)
x = data[str(xSelect.currentText())]
y = data[str(ySelect.currentText())]
if self.UIDic["LogXAxis"][UIIndex].isChecked():
#x = np.sign(x)* np.log10(abs(x) + 1)
x = np.log(x + abs(min(x)) + 1)
if self.UIDic["LogYAxis"][UIIndex].isChecked():
#y = np.sign(y)* np.log10(abs(y) + 1)
y = np.log(y + abs(min(y)) + 1)
curve.setData(x,y)
self.UIDic["PlotData"][UIIndex] = data
self.UIDic["Plots"][UIIndex].addItem(curve)
#Add axis labels
self.UIDic["Plots"][UIIndex].setLabel('left', str(self.UIDic["YAxisSelectors"][UIIndex].currentText()))
self.UIDic["Plots"][UIIndex].setLabel('bottom', str(self.UIDic["XAxisSelectors"][UIIndex].currentText()))
#Add reigons we cleared
if str(ySelect.currentText()) == "Events":
for reigon in self.UIDic["AverageReigons"][UIIndex]:
self.UIDic["Plots"][UIIndex].addItem(reigon)
def makeFigure():
"""Get a list of the axis objects and create a figure"""
ax, f = getSetup((10, 8), (3, 4), multz={8: 1, 10: 1})
subplotLabel(ax)
Tcell_pathname = path_here + "/data/flow/2019-11-08 monomer IL-2 Fc signaling/CD4 T cells - IL2-060 mono, IL2-060 dimeric"
NK_CD8_pathname = path_here + "/data/flow/2019-11-08 monomer IL-2 Fc signaling/NK CD8 T cells - IL2-060 mono, IL2-060 dimeric"
Tcell_sample, _ = importF2(Tcell_pathname, "A")
NK_CD8_sample, _ = importF2(NK_CD8_pathname, "A")
Tcell_sample = combineWells(Tcell_sample).subsample(0.2)
NK_CD8_sample = combineWells(NK_CD8_sample).subsample(0.2)
Tcell_sample = applyMatrix(Tcell_sample,
compMatrix('2019-11-08', '1', 'A'))
NK_CD8_sample = applyMatrix(NK_CD8_sample,
compMatrix('2019-11-08', '1', 'B'))
Tcell_sample = Tcell_sample.transform(
"tlog", channels=['VL1-H', 'VL4-H', 'BL1-H',
'BL3-H']) # Tlog transformations
NK_CD8_sample = NK_CD8_sample.transform(
"tlog", channels=['RL1-H', 'VL4-H', 'BL1-H',
'BL2-H']) # Tlog transformations
cd4_gate = ThresholdGate(6500.0, ['VL4-H'],
region='above') & ThresholdGate(8000.0, ['VL4-H'],
region='below')
ax[0] = Tcell_sample.plot(['VL4-H'], gates=cd4_gate, ax=ax[0]) # CD4
plt.title("Singlet Lymphocytes")
#ax.set(xlabel= "CD4", ylabel="Events")
plt.grid()
sampleCD4 = Tcell_sample.gate(cd4_gate)
Treg_gate = PolyGate([(4.2e3, 7.2e3), (6.5e03, 7.2e03), (6.5e03, 5.3e03),
(4.9e03, 5.3e03), (4.2e03, 5.7e03)],
('VL1-H', 'BL1-H'),
region='in',
name='treg')
Thelp_gate = PolyGate([(1.8e03, 3.1e03), (1.8e03, 4.9e03),
(6.0e03, 4.9e03), (6.0e03, 3.1e03)],
('VL1-H', 'BL1-H'),
region='in',
name='thelper')
_ = sampleCD4.plot(['VL1-H', 'BL1-H'],
gates=[Treg_gate, Thelp_gate],
gate_colors=['red', 'red'],
cmap=cm.jet,
ax=ax[1]) # CD4
plt.title("CD4+ Cells")
plt.xlabel("CD25")
plt.ylabel("FOXP3")
plt.grid()
#CD8+ Cells
CD3CD8gate = PolyGate([(7.5e3, 8.4e3), (4.7e3, 8.4e3), (4.7e03, 6.5e03),
(7.5e03, 6.5e03)], ('VL4-H', 'RL1-H'),
region='in',
name='treg')
_ = NK_CD8_sample.plot(['VL4-H', 'RL1-H'],
gates=CD3CD8gate,
gate_colors='red',
cmap=cm.jet,
ax=ax[2]) # CD3, CD8
plt.title("Singlet Lymphocytes")
plt.xlabel("CD3")
plt.ylabel("CD8")
plt.grid()
# NK Cells
NKgate = PolyGate([(4.8e3, 5.1e3), (5.9e3, 5.1e3), (5.9e03, 6.1e03),
(4.8e03, 6.1e03)], ('VL4-H', 'BL1-H'),
region='in',
name='treg')
CD56brightgate = PolyGate([(4.8e3, 6.3e3),
(5.9e3, 6.3e3), (5.9e03, 7.3e03),
(4.8e03, 7.3e03)], ('VL4-H', 'BL1-H'),
region='in',
name='treg')
_ = NK_CD8_sample.plot(['VL4-H', 'BL1-H'],
gates=[NKgate, CD56brightgate],
gate_colors=['red', 'red'],
cmap=cm.jet,
ax=ax[3]) # CD3, CD56
plt.title("Singlet Lymphocytes")
plt.xlabel("CD3")
plt.ylabel("CD56")
plt.grid()
# Gating for live cells
sample1A, _, _ = importF("4-23", "1", "A", 1, "IL2R", None)
sample2B, _, _ = importF("4-23", "1", "B", 2, "IL2R", None)
sample3C, _, _ = importF("4-23", "1", "C", 3, "IL2R", None)
panel1 = sample1A.transform(
"tlog", channels=['VL6-H', 'VL4-H', 'BL1-H', 'VL1-H',
'BL3-H']).subsample(0.2)
panel2 = sample2B.transform("tlog", channels=['VL4-H',
'BL3-H']).subsample(0.2)
panel3 = sample3C.transform("tlog", channels=['VL6-H', 'VL4-H',
'BL3-H']).subsample(0.2)
cd3cd4_gate = PolyGate([(5.0e03, 7.3e03), (5.3e03, 5.6e03),
(8.0e03, 5.6e03), (8.0e03, 7.3e03)],
('VL4-H', 'VL6-H'),
region='in',
name='cd3cd4')
_ = panel1.plot(['VL4-H', 'VL6-H'],
gates=cd3cd4_gate,
gate_colors=['red'],
cmap=cm.jet,
ax=ax[4]) # CD3, CD4
plt.title("Singlet Lymphocytes")
plt.xlabel("CD3")
plt.ylabel("CD4")
plt.grid()
samplecd3cd4 = panel1.gate(cd3cd4_gate)
thelp_gate = PolyGate([(0.2e03, 6.8e03), (0.2e03, 4.4e03),
(3.7e03, 4.4e03), (5.7e03, 5.9e03),
(5.7e03, 6.8e03)], ('VL1-H', 'BL1-H'),
region='in',
name='thelp')
treg_gate = PolyGate([(3.8e03, 4.4e03), (3.8e03, 3.0e03), (6.5e03, 2.9e03),
(6.5e03, 5.0e03), (5.7e03, 5.8e03)],
('VL1-H', 'BL1-H'),
region='in',
name='treg')
_ = samplecd3cd4.plot(['VL1-H', 'BL1-H'],
gates=[thelp_gate, treg_gate],
gate_colors=['red', 'red'],
cmap=cm.jet,
ax=ax[5]) # CD3, CD4
plt.title("CD3+CD4+ cells")
plt.xlabel("CD25")
plt.ylabel("CD127")
plt.grid()
nk_gate = PolyGate([(3.3e3, 5.4e3), (5.3e3, 5.4e3), (5.3e3, 7.3e3),
(3.3e3, 7.3e3)], ('VL4-H', 'BL3-H'),
region='in',
name='nk')
nkt_gate = PolyGate([(5.6e3, 5.1e3), (7.6e3, 5.1e3), (7.6e3, 7.1e3),
(5.6e3, 7.1e3)], ('VL4-H', 'BL3-H'),
region='in',
name='nkt')
_ = panel2.plot(['VL4-H', 'BL3-H'],
gates=[nk_gate, nkt_gate],
gate_colors=['red', 'red'],
cmap=cm.jet,
ax=ax[6]) # CD56 vs. CD3
samplenk = panel2.gate(nk_gate)
samplenkt = panel2.gate(nkt_gate)
plt.title("Singlet Lymphocytes")
plt.xlabel("CD3")
plt.ylabel("CD56")
plt.grid()
cd8_gate = PolyGate([(4.2e3, 5.7e3), (8.1e3, 5.7e3), (8.1e3, 8.0e3),
(4.2e3, 8.0e3)], ('VL4-H', 'VL6-H'),
region='in',
name='cd8')
_ = panel3.plot(['VL4-H', 'VL6-H'],
gates=cd8_gate,
gate_colors=['red'],
cmap=cm.jet,
ax=ax[7]) # CD8 vs. CD3
plt.title("Singlet Lymphocytes")
plt.xlabel("CD3")
plt.ylabel("CD8")
for i, axs in enumerate(ax):
if i == 0:
print(" ")
# weird error replace later, axs is not correct object type
# axs.set(xlabel='CD4',ylabel='Events')
elif i == 1:
axs.set_title('T Cell Gating')
axs.set(xlabel='CD25', ylabel='FOXP3')
elif i == 2:
axs.set_title('CD8+ Cells Gating')
axs.set(xlabel='CD3', ylabel='CD8')
elif i == 3:
axs.set_title('NK Cells Gating')
axs.set(xlabel='CD3', ylabel='CD56')
elif i == 4:
axs.set_title('CD3+CD4+ Gating')
axs.set(xlabel='CD3', ylabel='CD4')
elif i == 5:
axs.set_title('T reg and T Helper Gating')
axs.set(xlabel='CD25', ylabel='CD127')
elif i == 6:
axs.set_title('NK and NKT Gating')
axs.set(xlabel='CD3', ylabel='CD56')
elif i == 7:
axs.set_title('CD3+CD8+ Gating')
axs.set(xlabel='CD3', ylabel='CD8')
if i != 0:
axs.grid()
receptorPlot(ax[8])
IL2RahiLoPlot(ax[9])
return f
# Input_file points to specific FCS file
input_file = raw_input("Enter FCS file location: ")
# Load the flow data
flow_data = FCMeasurement(ID='Flow data', datafile=input_file)
# Print channel information
print flow_data.channels
# Print the number of events in the data
print 'Events in file: ', flow_data.shape[0]
# Primary gates
non_debris_polygate = PolyGate([(45000, 50000), (150000, 55000),
(200000, 75000), (230000, 150000),
(180000, 225000), (60000, 200000),
(30000, 100000)], ['FSC-A', 'SSC-A'],
region='in',
name='live')
non_debris_gated_flow_data = flow_data.gate(non_debris_polygate)
singles_polygate = PolyGate([(45000, 25000), (190000, 99000), (170000, 135000),
(55000, 43000)], ['FSC-A', 'FSC-H'],
region='in',
name='singles')
singles_flow_data = non_debris_gated_flow_data.gate(singles_polygate)
# Transform data
tsingles_flow_data = singles_flow_data.transform(
'hlog',
channels=[
'Alexa Fluor 405-A', 'Alexa Fluor 405-H', 'Alexa Fluor 405-W',
'Alexa Fluor 488-A', 'Alexa Fluor 488-H', 'Alexa Fluor 488-W'
# datafile = '[insert path to your own fcs file]'
# Load data
tsample = FCMeasurement(ID='Test Sample', datafile=datafile)
tsample = tsample.transform('hlog', channels=['Y2-A', 'B1-A', 'V2-A'], b=500.0)
# Create poly gate
from FlowCytometryTools import PolyGate
# HINT: If you have wx-python installed, then you can use the GUI to create the gate.
# You can launch the GUI by executing the command tsample.view_interactively()
gate1 = PolyGate([(-5.441e+02, 7.978e+03), (-8.605e+02, 6.739e+03),
(-5.811e+02, 4.633e+03), (1.502e+03, 5.118e+03),
(8.037e+02, 8.215e+03), (8.037e+02, 8.215e+03)],
('B1-A', 'Y2-A'),
region='in',
name='poly gate')
# Gate
gated_sample = tsample.gate(gate1)
inverted_sample = tsample.gate(~gate1) # Everything outside of gate1
# Plot
gated_sample.plot(('Y2-A', 'B1-A'),
gates=[gate1],
kind='scatter',
color='red',
alpha=0.9)
inverted_sample.plot(('Y2-A', 'B1-A'), kind='scatter', color='gray', alpha=0.9)
sgn1.channel_names
# In[5]:
## plot halo control 1
halo1.plot(['FSC-A', 'SSC-A'])
plt.show()
# In[6]:
## draw gate around healthiest cells
gate1= PolyGate([(50000,0),(80000,0), (80000,45000), (50000,45000)],
('FSC-A', 'SSC-A'), region='in')
halo1.plot(['FSC-A', 'SSC-A'], gates=gate1)
plt.show()
# In[7]:
##see population that falls inside the gate
halo1_gated=halo1.gate(gate1)
halo1_gated.plot(['FSC-A','SSC-A'], gates=gate1)
plt.show()
# In[8]:
tsample.plot(['PE-Texas Red-A', 'Pacific Blue-A'],
cmap=pl.cm.gist_rainbow,
bins=150)
#%%
import wx
tsample.view_interactively()
#%%
from FlowCytometryTools import PolyGate
gate1 = PolyGate([(3.864e+03, 4.313e+03), (-2.091e+02, 3.875e+03),
(-2.813e+01, 7.716e+03), (5.040e+03, 9.256e+03),
(7.778e+03, 6.499e+03), (7.778e+03, 6.499e+03),
(7.778e+03, 6.499e+03)],
('PE-Texas Red-A', 'Pacific Blue-A'),
region='in',
name='gate1')
gated_tsample = tsample.gate(gate1)
pl.figure(num=None, figsize=(5.1, 6.6), dpi=80, facecolor='w', edgecolor='k')
tsample.plot(['PE-Texas Red-A', 'Pacific Blue-A'],
kind='scatter',
color='gray',
alpha=0.6,
label='Original')
gated_tsample.plot(['PE-Texas Red-A', 'Pacific Blue-A'],
print ''
preP1log = custom_log(preP1_sample, ['FSC-A', 'SSC-A'])
print 'profile after preP1 gating and log10 for FSC and SSC'
print preP1log.data.describe()
print ''
# //////////////////////////////////////////////
# P1 gating based on FSC and SSC (Log10 space)
P1_gatevalue = [(4.481395264353071, 4.727180089479611),
(1.761395345415388, 3.4679208677492026),
(1.6699667767116002, 2.8259455782395824),
(3.7042524303708757, 2.8012542209507507),
(4.869966681344168, 4.529649231168959)]
P1gate_channel = ('FSC-A', 'SSC-A')
P1gate = PolyGate(P1_gatevalue, P1gate_channel, region='in', name='P1 gate')
P1_sample = preP1log.gate(P1gate)
print 'profile after P1 gating'
print P1_sample.data.describe()
print ''
# ////////////////////////////////////
# threshold gate to remove all negative mCherry (PE-Texas Red) and GFP (FITC) events
prePxth_value1 = 10.0**2.0065723832855875
prePxth_channel1 = 'PE-Texas Red-A'
prePxth_value2 = 10.0**1.426888943216241
prePxth_channel2 = 'FITC-A'
prePxgate1 = ThresholdGate(prePxth_value1, prePxth_channel1, region='above')
prePxgate2 = ThresholdGate(prePxth_value2, prePxth_channel2, region='above')
prePxgate = prePxgate1 & prePxgate2