def Fig2():
fig = plt.figure()
path_006 = mydir + '/data/Box1Fig/Sample_006/'
path_012 = mydir + '/data/Box1Fig/Sample_012/'
path_264 = mydir + '/data/Box1Fig/Sample_264/'
plate_006 = FCPlate.from_dir(ID='Demo Plate', path = path_006, parser='name')
plate_012 = FCPlate.from_dir(ID='Demo Plate', path = path_012, parser='name')
plate_264 = FCPlate.from_dir(ID='Demo Plate', path = path_264, parser='name')
plate_006 = plate_006.dropna()
plate_012 = plate_012.dropna()
plate_264 = plate_264.dropna()
plate_006 = plate_006.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
plate_012 = plate_012.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
plate_264 = plate_264.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold_006 = getDAPIgate(plate_006)
threshold_012 = getDAPIgate(plate_012)
threshold_264 = getDAPIgate(plate_264)
gate_006 = ThresholdGate(threshold_006, 'Pacific Blue-A', region='above')
gate_012 = ThresholdGate(threshold_012, 'Pacific Blue-A', region='above')
gate_264 = ThresholdGate(threshold_264, 'Pacific Blue-A', region='above')
gated_sample_006 = plate_006['A8'].gate(gate_006)
gated_sample_012 = plate_012['A8'].gate(gate_012)
gated_sample_264 = plate_264['A8'].gate(gate_264)
RSG_006 = gated_sample_006.data[['FITC-A']].values
RSG_012 = gated_sample_012.data[['FITC-A']].values
RSG_264 = gated_sample_264.data[['FITC-A']].values
#colors = ['#FF6347', '#FFA500', '#87CEEB']
plt.hist(RSG_006, 40, fc='#87CEEB', histtype='bar', alpha=0.5, normed=True)
plt.hist(RSG_012, 40, fc='#FFA500', histtype='bar', alpha=0.5, normed=True)
plt.hist(RSG_264, 40, fc='#FF6347', histtype='bar', alpha=0.5, normed=True)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
#plt.title('The distribution of reductase \n acivity in a microbial population', \
# fontsize = 20, weight = 'heavy')
plt.xlabel('Metabolic activity', fontsize = 18)
plt.ylabel('Frequency', fontsize = 18)
plt.arrow(2500, 0.00075, 3400, 0, width=0.00004, \
head_width=0.00012, head_length=450, length_includes_head=True, \
shape='full', color = '#87CEEB')
plt.arrow(5350, 0.00064, -3400, 0, width=0.00004, \
head_width=0.00012, head_length=450, length_includes_head=True, \
shape='full', color = '#FF6347')
plt.xlim(0, 8000)
plt.ylim(0, 0.001)
fig.tight_layout()
plt.gca().invert_xaxis()
plt.text(4800, 0.00055 , 'Initiation', color = '#FF6347', fontsize = 18, weight = 'heavy')
plt.text(5050, 0.0008, 'Resucitation', color = '#87CEEB', fontsize = 18, weight = 'heavy')
fig_name = mydir + '/figures/Fig2.png'
fig.savefig(fig_name, bbox_inches = "tight", pad_inches = 0.4, dpi = 600)
plt.close()
def get_dist_data():
makeFigFolders(analysis = 'RSG')
OUT1 = open(git_path +'/data/FlowSummary.txt', 'w+')
print>> OUT1, 'Sample', 'Mean', 'Std', 'Median', 'Skew'
for sample in samples:
sample_name = 'Sample_' + sample
path = data_path + 'Sample_' + sample + '/'
files = os.listdir(path)
remove = ['misc', '.DS_Store']
files1 = [i for i in files if i not in remove]
files_split = [re.split(r'[._]',x)[3] for x in files1]
path = data_path + 'Sample_' + sample + '/'
plate = FCPlate.from_dir(ID='Demo Plate', path = path, parser='name')
plate = plate.dropna()
plate = plate.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
DAPI_threshold = getDAPIgate(plate)
DAPI_gate = ThresholdGate(DAPI_threshold, 'Pacific Blue-A', region='above')
intersection_gate = DAPI_gate
DAPI_gated_A9 = plate['A8'].gate(intersection_gate)
# RSG
RSG = DAPI_gated_A9.data[['FITC-A']].values
print>> OUT1, sample_name, str(np.mean(RSG)), str(np.std(RSG)), \
str(np.median(RSG)), str(stats.skew(RSG)[0])
OUT1.close()
def get_RSG_movie():
# Set up formatting for the movie files
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
number_of_frames = len(samples)
for sample in samples:
print sample
path = data_path + 'Sample_' + sample + '/'
plate = FCPlate.from_dir(ID='Demo Plate', path = path, parser='name')
plate = plate.dropna()
plate = plate.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold = getDAPIgate(plate)
gate = ThresholdGate(threshold, 'Pacific Blue-A', region='above')
gated_sample = plate['A8'].gate(gate)
RSG = gated_sample.data[['FITC-A']].values
l, = plt.plot([], [], 'r-')
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel('x')
plt.title('test')
line_ani = animation.FuncAnimation(fig1, update_line, 25, fargs=(data, l),
interval=50, blit=True)
line_ani.save('lines.mp4', writer=writer)
def update_hist(num, data):
plt.cla()
plt.hist(data[num])
fig = plt.figure()
hist = plt.hist(RSG, 30, fc='gray', histtype='stepfilled', alpha=0.3, normed=True)
def gating(cell_type, date=False, Mut=False, rep=0):
""" Creates and returns the cell type gate on CD4+ cells. """
if not Mut:
cell1 = QuadGate(vert[cell_type][0],
channels[cell_type],
region=regionSpec[cell_type][0],
name=(cell_type + "1"))
cell2 = QuadGate(vert[cell_type][1],
channels[cell_type],
region=regionSpec[cell_type][1],
name=(cell_type + "2"))
else:
cell1 = import_gates_pSTAT(date, cell_type, rep)
if regionSpec_[cell_type] is not None:
cd45 = ThresholdGate(6300, ("BL3-H"),
region=regionSpec_[cell_type],
name="cd45")
gate = cell1 & cell2 & cd4() & cd45
else:
if cell_type in ("nk", "nkt", "bnk", "cd"):
if Mut:
gate = cell1
else:
gate = cell1 & cell2
else:
if Mut:
gate = cell1 & cd4monMut()
else:
gate = cell1 & cell2 & cd4()
return gate
def threshold_gate(self, channel_name=''):
if not channel_name:
channel_name = self.channel_names[0]
# Check if file already exists
if CHECK_IF_FILE_EXIST:
if self.check_if_images_exist('threshold_gate'):
return True
# Create a threshold gates
y2_gate = ThresholdGate(1000.0, channel_name, region='above')
# Gate
gated_sample = self.sample.gate(y2_gate)
# Plot
ax1 = subplot(121)
self.sample.plot(channel_name,
gates=[y2_gate],
bins=self.bins,
alpha=0.9)
y2_gate.plot(color='k', linewidth=4, linestyle='-')
title('Original Sample')
ax2 = subplot(122, sharey=ax1, sharex=ax1)
gated_sample.plot(channel_name,
gates=[y2_gate],
bins=self.bins,
color='y',
alpha=0.9)
title('Gated Sample')
tight_layout()
png_file = os.path.join(SHARED_PLOTTING_DIR,
self.get_file_name('threshold_gate.png'))
print(png_file)
grid(True)
savefig(png_file)
self.response['threshold_gate'] = self.get_file_name(
'threshold_gate.png')
def plate_gated_median_fluorescence(self,
channel_name1='',
channel_name2=''):
if not channel_name1:
channel_name1 = self.channel_names[0]
channel_name2 = self.channel_names[1]
# Check if file already exists
if CHECK_IF_FILE_EXIST:
if self.check_if_images_exist('plate_gated_median_fluorescence'):
return True
# Load plate
plate = FCPlate.from_dir(ID='Demo Plate',
path=SHARED_RAW_DIR,
parser='name')
plate = plate.transform('hlog',
channels=[channel_name1, channel_name2],
b=500.0)
# Clear prev sub plot
subplots(clear=True)
# Drop empty cols / rows
plate = plate.dropna()
# Create a threshold gates
y2_gate = ThresholdGate(1000.0, channel_name1, region='above')
# Plot
plate = plate.gate(y2_gate)
output = plate.apply(self.__calculate_median_Y2)
plot_heat_map(output,
include_values=True,
show_colorbar=True,
cmap=cm.Reds)
title('Heat map of median RFP fluorescence on plate')
png_file = os.path.join(
SHARED_PLOTTING_DIR,
self.get_file_name('plate_gated_median_fluorescence.png'))
print(png_file)
grid(False)
savefig(png_file)
self.response['plate_gated_median_fluorescence'] = self.get_file_name(
'plate_gated_median_fluorescence.png')
def plate_gated_counts(self, channel_name1='', channel_name2=''):
if not channel_name1:
channel_name1 = self.channel_names[0]
channel_name2 = self.channel_names[1]
# Check if file already exists
if CHECK_IF_FILE_EXIST:
if self.check_if_images_exist('plate_gated_counts'):
return True
# Plot - Counting fluorescent events¶
self.sample.plot([channel_name1, channel_name2],
kind='scatter',
alpha=0.6,
color='gray')
# Clear prev sub plot
subplots(clear=True)
# Load plate
plate = FCPlate.from_dir(ID='Demo Plate',
path=SHARED_RAW_DIR,
parser='name')
plate = plate.transform('hlog',
channels=[channel_name1, channel_name2],
b=500.0)
# Drop empty cols / rows
plate = plate.dropna()
# Create a threshold gates
y2_gate = ThresholdGate(1000.0, channel_name1, region='above')
# Plot
plate = plate.gate(y2_gate)
plot_heat_map(plate.counts(),
include_values=True,
show_colorbar=True,
cmap=cm.Oranges)
title('Heat map of fluorescent counts on plate')
png_file = os.path.join(SHARED_PLOTTING_DIR,
self.get_file_name('plate_gated_counts.png'))
print(png_file)
grid(False)
savefig(png_file)
self.response['plate_gated_counts'] = self.get_file_name(
'plate_gated_counts.png')
def animate(i):
sample = samples[i]
path = data_path + 'Sample_' + sample + '/'
plate = FCPlate.from_dir(ID='Demo Plate', path=path, parser='name')
plate = plate.dropna()
plate = plate.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold = getDAPIgate(plate)
gate = ThresholdGate(threshold, 'Pacific Blue-A', region='above')
gated_sample = plate['A8'].gate(gate)
RSG = gated_sample.data[['FITC-A']].values
# simulate new data coming in
n, bins = np.histogram(RSG, 40, normed=True)
top = bottom + n
verts[1::5, 1] = top
verts[2::5, 1] = top
#t = ax.annotate(sample + ' hours',(7900,1200)) # add text
time_text.set_text(sample + ' hours')
return [patch, time_text]
def get_RSG_hist():
folder_path = git_path + '/figures/flow_figs/RSG'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
fig1 = plt.figure()
for sample in samples:
print sample
path = data_path + 'Sample_' + sample + '/'
plate = FCPlate.from_dir(ID='Demo Plate', path = path, parser='name')
plate = plate.dropna()
plate = plate.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold = getDAPIgate(plate)
gate = ThresholdGate(threshold, 'Pacific Blue-A', region='above')
gated_sample = plate['A8'].gate(gate)
RSG = gated_sample.data[['FITC-A']].values
fig, ax = plt.subplots()
ax.hist(RSG, 30, fc='gray', histtype='stepfilled', alpha=0.3, normed=True)
axes.set_xlim([0,10000])
plt.savefig(git_path + '/figures/flow_figs/RSG/RSG_Sample_' + sample + '.png')
plt.close()
figure()
tsample.plot(['B1-A', 'Y2-A'], cmap=cm.Oranges, colorbar=False)
# Plotting 2D scatter plots
figure()
tsample.plot(['B1-A', 'Y2-A'], kind='scatter', color='red', s=1, alpha=0.3)
# Gating
from FlowCytometryTools import ThresholdGate, PolyGate
# Creating gates programmatically
y2_gate = ThresholdGate(1000.0, ['Y2-A'], region='above')
b1_gate = ThresholdGate(2000.0, ['B1-A'], region='above')
# Plotting gates
figure()
tsample.plot(['Y2-A'], gates=[y2_gate], bins=100)
title('Gate Plotted')
# Applying gates
gated_sample = tsample.gate(y2_gate)
print(gated_sample.get_data().shape[0])
# The gated_sample is also an instance of FCMeasurement
figure()
('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
gatePB = ThresholdGate(4.024e+00, ('Pacific Blue-A'), region='above')
Comps.samples[3] = Comps.samples[3].gate(gatePB)
#%% calculate compensation matrix
IdxMatch = [0, 3, 4, 1] #make diagonal, index of channel in samples
IndBlank = 2
meansMatrix = Comps.aSinhmeans().as_matrix()[IdxMatch].T
BGMatrix = np.tile(Comps.aSinhmeans().as_matrix()[IndBlank],
(np.shape(meansMatrix)[1], 1)).T
Hu = meansMatrix - BGMatrix
SpilloverM = Hu / Hu.diagonal()
SpilloverM = np.matrix(SpilloverM)
from numpy.linalg import inv
metaxls = pd.ExcelFile(metadataDir)
metadata = {
sheet: metaxls.parse(sheet, index_col=0)
for sheet in metaxls.sheet_names
} #import all sheets in metadata file into a dict, property name=keys, metadata df = values
#plateNameCore = '' #for debugging
fcsFolderDir = plateNameCore + '_FCS'
datadir = os.path.join(dataRootDir, dataFolderDir, fcsFolderDir)
plate = FCPlate.from_dir(ID='Plate',
path=datadir,
parser=cf.fcsNameParser,
position_mapper='name')
# Gating
fsc_gate = ThresholdGate(1000.0, ['FSC-H'], region='above')
ssc_gate = ThresholdGate(1000.0, ['SSC-H'], region='above')
rfpA_gate = ThresholdGate(1.0, ['RFP2-A'], region='above')
fscssc_gate = CompositeGate(fsc_gate, 'and', ssc_gate)
plate = plate.gate(fscssc_gate)
plate = plate.gate(rfpA_gate)
# Produces 96 well layout for intuitive observation
# Calculate Median from data
def calculate_median_Y2(well):
return well.data['RFP2-H'].median()
dfAll = {}
dfAll['RFP'] = plate.apply(calculate_median_Y2)
dfAll['Count'] = plate.counts()
def cd4monMut():
""" Function for gating CD4+ cells (generates T cells) - for monomeric Mut Experiments. """
cd41 = ThresholdGate(6400.0, ("VL4-H"), region="above", name="cd41")
cd42 = ThresholdGate(8000.0, ("VL4-H"), region="below", name="cd42")
cd4_gate = cd41 & cd42
return cd4_gate
def cd4():
""" Function for gating CD4+ cells (generates T cells). """
cd41 = ThresholdGate(6.514e03, ("VL4-H"), region="above", name="cd41")
cd42 = ThresholdGate(7.646e03, ("VL4-H"), region="below", name="cd42")
cd4_gate = cd41 & cd42
return cd4_gate
import FlowCytometryTools
from FlowCytometryTools import FCMeasurement
from FlowCytometryTools import ThresholdGate
# Locate sample data included with this package
datadir = os.path.join(FlowCytometryTools.__path__[0], 'tests', 'data', 'Plate01')
datafile = os.path.join(datadir, 'RFP_Well_A3.fcs')
# 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 a threshold gates
y2_gate = ThresholdGate(1000.0, 'Y2-A', region='above')
# Gate
gated_sample = tsample.gate(y2_gate)
# Plot
ax1 = subplot(121);
tsample.plot('Y2-A', gates=[y2_gate], bins=100, alpha=0.9);
y2_gate.plot(color='k', linewidth=4, linestyle='-')
title('Original Sample');
ax2 = subplot(122, sharey=ax1, sharex=ax1);
gated_sample.plot('Y2-A', gates=[y2_gate], bins=100, color='y', alpha=0.9);
title('Gated Sample');
tight_layout()
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'
],
b=500.0)
# Continue gating data
cfp_expressing_gate = ThresholdGate(2000.0,
'Alexa Fluor 405-A',
region='above')
cfp_expressing_flow_data = tsingles_flow_data.gate(cfp_expressing_gate)
non_cfp_expressing_gate = ThresholdGate(2000.0,
'Alexa Fluor 405-A',
region='below')
non_cfp_expressing_flow_data = tsingles_flow_data.gate(non_cfp_expressing_gate)
cfp_expressing_cell_cycle_polygate = PolyGate([(65000, 23000), (115000, 23000),
(115000, 120000),
(65000, 120000)],
['CyChrome-W', 'CyChrome-A'],
region='in',
name='cell cycle')
cfp_expressing_cell_cycle_flow_data = cfp_expressing_flow_data.gate(
cfp_expressing_cell_cycle_polygate)
non_cfp_expressing_cell_cycle_polygate = PolyGate([(65000, 23000),
def calculate_cell_cycle_status():
# Gate for G1 and G2 peaks (CFP expressing)
cfp_cell_cycle_g1_gate = ThresholdGate(65000.0,
'CyChrome-A',
region='below')
cfp_cell_cycle_g1_flow_data = cfp_expressing_cell_cycle_flow_data.gate(
cfp_cell_cycle_g1_gate)
cfp_cell_cycle_g2_gate = ThresholdGate(89000.0,
'CyChrome-A',
region='above')
cfp_cell_cycle_g2_flow_data = cfp_expressing_cell_cycle_flow_data.gate(
cfp_cell_cycle_g2_gate)
# Gate for S phase, isolating the data between G1 and G2 peaks (CFP expressing)
cfp_cell_cycle_s_a_gate = ThresholdGate(65000.0,
'CyChrome-A',
region='above')
cfp_cell_cycle_s_flow_data = cfp_expressing_cell_cycle_flow_data.gate(
cfp_cell_cycle_s_a_gate)
cfp_cell_cycle_s_b_gate = ThresholdGate(89000.0,
'CyChrome-A',
region='below')
cfp_cell_cycle_s_flow_data = cfp_cell_cycle_s_flow_data.gate(
cfp_cell_cycle_s_b_gate)
cfp_cells_in_g1 = cfp_cell_cycle_g1_flow_data.shape[0]
cfp_cells_in_g2 = cfp_cell_cycle_g2_flow_data.shape[0]
cfp_cells_in_s = cfp_cell_cycle_s_flow_data.shape[0]
cfp_total_cells = cfp_cells_in_g1 + cfp_cells_in_g2 + cfp_cells_in_s
cfp_percent_g1 = 100 * (cfp_cells_in_g1 / cfp_total_cells)
cfp_percent_g2 = 100 * (cfp_cells_in_g2 / cfp_total_cells)
cfp_percent_s = 100 * (cfp_cells_in_s / cfp_total_cells)
print 'CFP expressing cells in G1: ' + str(cfp_percent_g1) + ' %'
print 'CFP expressing cells in G2: ' + str(cfp_percent_g2) + ' %'
print 'CFP expressing cells in S: ' + str(cfp_percent_s) + ' %'
print '-------------------------------'
# Gate for G1 and G2 peaks (non-GFP expressing)
non_cfp_cell_cycle_g1_gate = ThresholdGate(65000.0,
'CyChrome-A',
region='below')
non_cfp_cell_cycle_g1_flow_data = non_cfp_expressing_cell_cycle_flow_data.gate(
non_cfp_cell_cycle_g1_gate)
non_cfp_cell_cycle_g2_gate = ThresholdGate(89000.0,
'CyChrome-A',
region='above')
non_cfp_cell_cycle_g2_flow_data = non_cfp_expressing_cell_cycle_flow_data.gate(
non_cfp_cell_cycle_g2_gate)
# Gate for S phase, isolating the data between G1 and G2 peaks (non-GFP expressing)
non_cfp_cell_cycle_s_a_gate = ThresholdGate(65000.0,
'CyChrome-A',
region='above')
non_cfp_cell_cycle_s_flow_data = non_cfp_expressing_cell_cycle_flow_data.gate(
non_cfp_cell_cycle_s_a_gate)
non_cfp_cell_cycle_s_b_gate = ThresholdGate(89000.0,
'CyChrome-A',
region='below')
non_cfp_cell_cycle_s_flow_data = non_cfp_cell_cycle_s_flow_data.gate(
non_cfp_cell_cycle_s_b_gate)
non_cfp_cells_in_g1 = non_cfp_cell_cycle_g1_flow_data.shape[0]
non_cfp_cells_in_g2 = non_cfp_cell_cycle_g2_flow_data.shape[0]
non_cfp_cells_in_s = non_cfp_cell_cycle_s_flow_data.shape[0]
non_cfp_total_cells = non_cfp_cells_in_g1 + non_cfp_cells_in_g2 + non_cfp_cells_in_s
non_cfp_percent_g1 = 100 * (non_cfp_cells_in_g1 / non_cfp_total_cells)
non_cfp_percent_g2 = 100 * (non_cfp_cells_in_g2 / non_cfp_total_cells)
non_cfp_percent_s = 100 * (non_cfp_cells_in_s / non_cfp_total_cells)
print 'Non-CFP expressing cells in G1: ' + str(non_cfp_percent_g1) + ' %'
print 'Non-CFP expressing cells in G2: ' + str(non_cfp_percent_g2) + ' %'
print 'Non-CFP expressing cells in S: ' + str(non_cfp_percent_s) + ' %'
cutoffs.append(DAPI_gate)
cutoff = np.mean(cutoffs)
return cutoff
fig, ax = plt.subplots()
# histogram our data with numpy
path1 = data_path + 'Sample_' + samples[0] + '/'
plate1 = FCPlate.from_dir(ID='Demo Plate', path=path1, parser='name')
plate1 = plate1.dropna()
plate1 = plate1.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold = getDAPIgate(plate1)
gate1 = ThresholdGate(threshold, 'Pacific Blue-A', region='above')
gated_sample1 = plate1['A8'].gate(gate1)
RSG1 = gated_sample1.data[['FITC-A']].values
n, bins = np.histogram(RSG1, 40, normed=True)
# get the corners of the rectangles for the histogram
left = np.array(bins[:-1])
right = np.array(bins[1:])
bottom = np.zeros(len(left))
top = bottom + n
nrects = len(left)
# here comes the tricky part -- we have to set up the vertex and path
# codes arrays using moveto, lineto and closepoly
# for each rect: 1 for the MOVETO, 3 for the LINETO, 1 for the
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
from FlowCytometryTools import FCMeasurement, ThresholdGate
import os, FlowCytometryTools
from pylab import *
# Locate sample data included with this package
datadir = os.path.join(FlowCytometryTools.__path__[0], 'tests', 'data', 'Plate01')
datafile = os.path.join(datadir, 'RFP_Well_A3.fcs')
# 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 a threshold gates
y2_gate = ThresholdGate(1000.0, 'Y2-A', region='above')
# Gate
gated_sample = tsample.gate(y2_gate)
# Plot
ax1 = subplot(121);
tsample.plot('Y2-A', gates=[y2_gate], bins=100, alpha=0.9);
y2_gate.plot(color='k', linewidth=4, linestyle='-')
title('Original Sample');
ax2 = subplot(122, sharey=ax1, sharex=ax1);
gated_sample.plot('Y2-A', gates=[y2_gate], bins=100, color='y', alpha=0.9);
title('Gated Sample');
tight_layout()
from GoreUtilities import plot_heat_map
import os, FlowCytometryTools
from pylab import *
import matplotlib.pyplot as plt
# Locate sample data included with this package
datadir = os.path.join(FlowCytometryTools.__path__[0], 'tests', 'data', 'Plate01')
# datadir = '[insert path to your own folder with fcs files.]'
# Make sure that the files follow the correct naming convention for the 'name' parser.
# Alternatively, change the parser (see tutorial online for further information).
# Load plate
plate = FCPlate.from_dir(ID='Demo Plate', path=datadir, parser='name')
plate = plate.transform('hlog', channels=['Y2-A', 'B1-A'], b=500.0)
# Drop empty cols / rows
plate = plate.dropna()
# Create a threshold gates
from FlowCytometryTools import ThresholdGate
y2_gate = ThresholdGate(1000.0, 'Y2-A', region='above')
# Plot
plate = plate.gate(y2_gate)
plot_heat_map(plate.counts(), include_values=True, show_colorbar=True,
cmap=cm.Oranges)
title('Heat map of fluorescent counts on plate')
#show() # <-- Uncomment when running as a script.
def testCounts():
tubes = ['A8']
for sample in samples:
print sample
path = data_path + 'Sample_' + sample + '/'
files = os.listdir(path)
remove = ['misc', '.DS_Store']
files1 = [i for i in files if i not in remove]
files_split = [re.split(r'[._]',x)[3] for x in files1]
if 'H' in files_split:
for tube in tubes:
file_folder_path = git_path + '/figures/flow_figs/' + tube
if not os.path.exists(file_folder_path):
os.makedirs(file_folder_path)
file_folder_sample_path = git_path + '/figures/flow_figs/' + \
tube +'/Sample_' + sample + '/'
if not os.path.exists(file_folder_sample_path):
os.makedirs(file_folder_sample_path)
plate = FCPlate.from_dir(ID='Demo Plate', path = path, parser='name')
plate = plate.dropna()
plate = plate.transform('hlog', channels=['FSC-A', 'SSC-A', \
'FSC PMT-A','PI (YG)-A', 'FITC-A', 'Pacific Blue-A', 'APC-A'])
threshold = getDAPIgate(plate)
gate = ThresholdGate(threshold, 'Pacific Blue-A', region='above')
gated_sample = plate[tube].gate(gate)
FSC_A = gated_sample.data[['FSC-A']].values
SSC_A = gated_sample.data[['SSC-A']].values
PI = gated_sample.data[['PI (YG)-A']].values
FITC = gated_sample.data[['FITC-A']].values
FSC_H = gated_sample.data[['FSC-H']].values
SSC_H = gated_sample.data[['SSC-H']].values
FSC_PMT_A = gated_sample.data[['FSC PMT-A']].values
FSC_PMT_H = gated_sample.data[['FSC PMT-H']].values
Pacific_blue = gated_sample.data[['Pacific Blue-A']].values
APC = gated_sample.data[['APC-A']].values
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(FITC, Pacific_blue, APC , zdir='z', alpha = 0.2)
ax.set_xlabel('FITC')
ax.set_ylabel('Pacific blue')
ax.set_zlabel('APC')
name = tube + '_FITC_APC_PacificBlue_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(APC, FITC, alpha = 0.2)
ax.set_xlabel('APC')
ax.set_ylabel('FITC')
name = tube +'_APC_FITC_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(Pacific_blue, FITC, alpha = 0.2)
ax.set_xlabel('Pacific blue')
ax.set_ylabel('FITC')
name = tube + '_PacificBlue_FITC_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(FSC_A, SSC_A, alpha = 0.2)
ax.set_xlabel('FSC-A')
ax.set_ylabel('SSC-A')
name = tube + '_FSC_A_SSC_A_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(FSC_H, SSC_H, alpha = 0.2)
ax.set_xlabel('FSC-H')
ax.set_ylabel('SSC-H')
#plt.axhline(y = 250000, linewidth=2, color='darkgrey', ls='--')
name = tube + '_FSC_H_SSC_H_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(FSC_PMT_A, FSC_PMT_H, alpha = 0.2)
ax.set_xlabel('FSC PMT-A')
ax.set_ylabel('FSC PMT-H')
name = tube + '_FSC_PMT_A_FSC_PMT_H_' + sample
plt.savefig(file_folder_sample_path + name + '.png')
plt.close()
from scipy.sparse import hstack
import numpy as np
from scipy.stats import gaussian_kde
plate = FCPlate.from_dir(
ID='Demo Plate',
path=
'/Users/WRShoemaker/github/Task2/FlowCyto/20151203/CTC_FVD_Hoechst_test120315/',
parser='name')
plate = plate.transform('hlog',
channels=[
'FSC-A', 'SSC-A', 'PI (B)-A', 'Alexa Fluor 488-A',
'Pacific Blue-A'
])
plate = plate.dropna()
gate = ThresholdGate(2000.0, 'Pacific Blue-A', region='below')
gated_sample_beads_A3 = plate['A3'].gate(gate)
plateData = plate['A3'].data[['Pacific Blue-A', 'PI (B)-A']]
CTCnumpy = gated_sample_beads_A3[['PI (B)-A']].values
DNAnumpy = gated_sample_beads_A3[['Pacific Blue-A']].values
def get_kdens_choose_kernel(xlist, expand, kernel=0.5):
""" Finds the kernel density function across a vector of values """
xlist = xlist[np.logical_not(np.isnan(xlist))]
density = gaussian_kde(xlist)
n = len(xlist)
if expand == False:
xs = np.linspace(min(xlist), max(xlist), n)
# Our transformation goes here
for channel in channelLst:
new_data[channel] = np.log10(original_data[channel])
new_data = new_data.dropna() # Removes all NaN entries
new_sample.data = new_data
return new_sample
# //////////////////////////////////////////////
# threshold gating to remove all negative FSC and SSC events
preP1th_value1 = 10.0**1.6699667767116002
preP1th_channel1 = 'FSC-A'
preP1th_value2 = 10.0**2.8012542209507507
preP1th_channel2 = 'SSC-A'
preP1gate1 = ThresholdGate(preP1th_value1, preP1th_channel1, region='above')
preP1gate2 = ThresholdGate(preP1th_value2, preP1th_channel2, region='above')
preP1gate = preP1gate1 & preP1gate2
preP1_sample = sample.gate(preP1gate)
print 'profile after preP1 gating'
print preP1_sample.data.describe()
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)