Demo: analysis of flow cytometry data at single-cell resolution

# Import library
from FlowCytometryTools import FCMeasurement

# load fcs file (version 3)
sample = FCMeasurement(ID='Example sample', datafile="data/example.fcs")

# Let's see the number of cells measured
sample.counts

# All metadata
sample.meta.items()[:10] # see only first 10 entries

sample.channel_names

sample.channels

# get forward vs side scatter of first 10 cells
sample.data[['FSC-A', 'SSC-A']][:10]

# Get all channels in one table
# this would be the main table to work on from here
sample.data.drop("Time", axis=1).head(10)  # show only first 10 cells

# Let's plot some things:

# plot with provided library wrappers
_ = sample.plot(['FSC-A', 'SSC-A'], bins=1000)

# we can obviously also plot with base matplotlib + seaborn
import matplotlib.pyplot as plt
import pylab
import seaborn as sns
sns.set_style("whitegrid")
sns.jointplot(sample.data['FSC-A'], sample.data['SSC-A'],
              xlim=(0, 250000), ylim=(0, 250000),
              alpha=0.01)

# Now let's make a gate using the interactive interface
# sample.view_interactively()

from FlowCytometryTools import ThresholdGate, PolyGate

# Four threshold gates
gate1 = ThresholdGate(17500, 'FSC-A', region='above')
gate2 = ThresholdGate(60000, 'SSC-A', region='below')
gate3 = ThresholdGate(110000, 'FSC-A', region='below')
gate4 = ThresholdGate(10000, 'SSC-A', region='above')

# Similar thing with a polygon
# drawn interactively
gate5 = PolyGate(
    [(3.140e+04, 9.951e+04), (1.108e+04, 5.092e+04), (1.421e+04, 3.304e+04),
     (2.385e+04, 2.426e+04), (3.219e+04, 1.583e+04), (3.818e+04, 5.706e+03),
     (5.251e+04, 4.019e+03), (1.208e+05, 1.178e+04), (1.408e+05, 5.429e+04),
     (3.505e+04, 9.681e+04), (3.114e+04, 9.951e+04), (3.219e+04, 9.613e+04)],
    ('FSC-A', 'SSC-A'), region='in', name='gate4')
_ = sample.plot(['FSC-A', 'SSC-A'], bins=1000, gates=[gate1, gate2, gate3, gate4, gate5])

# Plot channels individually
_ = sample.plot('FSC-A', bins=1000)

# Log transform and plot again
_ = sample.transform('hlog', channels=['FSC-A']).plot('FSC-A', bins=1000)

# Log transform and plot different channel
_ = sample.transform('hlog', channels=['R/B Alexa Fluor 700-A']).plot('R/B Alexa Fluor 700-A', bins=1000)