def test_plot_sample_scatter(self):
sample = fk.Sample(fcs_path)
xform_logicle = fk.transforms.LogicleTransform('logicle',
param_t=10000,
param_w=0.5,
param_m=4.5,
param_a=0)
sample.apply_transform(xform_logicle)
p = sample.plot_scatter('FL1-H', 'FL2-H', source='xform')
self.assertIsInstance(p, Figure)
#reading, transforming and manipulating FCS files
#Date Modified: Noveber 24
#Make sure you have Flow Kit installed
import flowkit as fk
import numpy as np
import pandas as pd
#here is the fcs file we will read.
fcs_path = 'sampleFCS.fcs'
#read in the FCS file
sample = fk.Sample(fcs_path)
#print to see how manny cells and channels there are
#doing so tells us that there are 268796 cells (events) and 49 channels
print(sample)
#convert to numpy array
dm = sample.get_raw_events()
#now we will look at the channel names. This is helpful because we will get comprehsnible marker names.
#we will then be only selecting channel corresponding to our marker of interest
CNames = np.array(sample.pns_labels)
#for downstream analysis we will keep the following channels corresponding to phenotypic and functional markers
#you will need to customize this for your own analysis
toKeep = [
9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45
]
def test_sample_repr(self):
fcs_file_path = "examples/data/gate_ref/data1.fcs"
sample = fk.Sample(fcs_path_or_data=fcs_file_path)
sample_string = "Sample(v2.0, B07, 8 channels, 13367 events)"
self.assertEqual(repr(sample), sample_string)
"""
Unit tests for string representations
"""
import unittest
import flowkit as fk
from . import gating_strategy_prog_gate_tests as prog_test_data
# import comp_matrix_01, logicle_xform1, hyperlog_xform1, poly1_gate
data1_fcs_path = 'examples/data/gate_ref/data1.fcs'
data1_sample = fk.Sample(data1_fcs_path)
class StringReprTestCase(unittest.TestCase):
"""Tests related to string representations of FlowKit classes"""
def test_vert_repr(self):
vert = fk.Vertex([500, 5])
vert_string = "Vertex([500, 5])"
self.assertEqual(repr(vert), vert_string)
def test_dim_repr(self):
poly1_dim1 = fk.Dimension('FL2-H', compensation_ref='FCS')
dim_string = "Dimension(id: FL2-H)"
self.assertEqual(repr(poly1_dim1), dim_string)
def test_ratio_dim_repr(self):
dim_rat1 = fk.RatioDimension(
'FL2Rat1',
compensation_ref='uncompensated',
range_min=3,
"""
Unit tests for plotting functions
"""
import copy
import unittest
from bokeh.plotting.figure import Figure as bk_Figure
from bokeh.layouts import Column as bk_Column
from matplotlib.pyplot import Figure as mpl_Figure
import flowkit as fk
fcs_path = 'examples/data/gate_ref/data1.fcs'
gml_path = 'examples/data/gate_ref/gml/gml_all_gates.xml'
test_sample = fk.Sample(fcs_path, subsample=2000)
test_gating_strategy = fk.parse_gating_xml(gml_path)
class PlotTestCase(unittest.TestCase):
"""
Tests for plot functions/methods
NOTE: Due to the difficulty of introspecting figures and images at a
pixel-level, this TestCase only tests that plots are returned
from plotting functions.
"""
def test_plot_sample_histogram(self):
sample = copy.deepcopy(test_sample)
xform_logicle = fk.transforms.LogicleTransform('logicle', param_t=10000, param_w=0.5, param_m=4.5, param_a=0)
sample.apply_transform(xform_logicle)
p = sample.plot_histogram(
# choose for development
# fcs_group = [fcs_group[0]]
# loop over tissue-specific fcs files and store sample data in dict
data_dict = {}
for fname in fcs_group:
print(f'Storing data for {fname}')
# extract sample metadata
sample_metadata = fname.split('.fcs')[0]
status = sample_metadata.split('_')[3]
timepoint = sample_metadata.split('_')[1]
replicate = sample_metadata.split('_')[4]
# store cmpvs and tp-specific unstained fcs files as variables
sample = fk.Sample(os.path.join(cmpvs_path, fname))
fiducial = fk.Sample(
os.path.join(raw_path, f'control_{timepoint}_unstained.fcs')
)
if channel not in ['fsc', 'ssc']:
# apply logicle transformation to fcs files
sample.apply_transform(xform)
fiducial.apply_transform(xform)
# get indices for current channel
sample_channel_label = channel_metadata[channel][0]
sample_channel_idx = sample.get_channel_index(sample_channel_label)
if channel not in ['fsc', 'ssc']:
# choose for development
# fcs_files = fcs_files[0:3]
# append zeroed fcs file to dataframe
for fname in fcs_files:
print(f'Processing data for {fname}')
sample_metadata = fname.split('.fcs')[0]
timepoint = int(sample_metadata.split('_')[1])
tissue = sample_metadata.split('_')[2]
status = sample_metadata.split('_')[3]
replicate = int(sample_metadata.split('_')[4])
# assign fcs file as a variable
sample = fk.Sample(os.path.join(cmpvs_path, fname))
# initialize dataframe to store zeroed sample data
df_temp = pd.DataFrame()
for channel in sorted(channel_metadata.keys()):
if channel not in ['fsc', 'ssc']:
# define logicle transformation model
param_w = channel_metadata[channel][1]
xform = fk.transforms.LogicleTransform('logicle',
param_t=262144.0,
param_w=param_w,
param_m=4.5,
param_a=0)
def main():
""" Main function """
data = []
time = []
for path in PATHS:
sample = fk.Sample(path)
data.append(load_data(sample))
time.append(load_time(sample))
sources = []
for i, e in enumerate(data):
sources.extend([i] * len(e))
data = np.concatenate(data, axis=0)
time = np.concatenate(time, axis=0)
d = len(data[0])
# Initialize a new Annoy object and index it using 10 trees
annoy = AnnoyIndex(d, metric="angular")
for i, v in enumerate(data):
annoy.add_item(i, v)
annoy.build(10)
# Create the k-nearest neighbor graph (k = 10)
edge_list = []
for i in range(len(data)):
for j in annoy.get_nns_by_item(i, 10):
edge_list.append((i, j, cosine_distance(data[i], data[j])))
# Compute the layout from the edge list
x, y, s, t, _ = tm.layout_from_edge_list(len(data), edge_list)
legend_labels = [(0, "No Target Probe Negative Control"),
(1, "Stained Sample")]
# Create the plot
faerun = Faerun(
view="front",
coords=False,
legend_title=
"RNA Flow Cytometry: evaluation of detection sensitivity in low abundant intracellular RNA ",
)
faerun.add_scatter(
"CYTO",
{
"x": x,
"y": y,
"c": sources,
"labels": sources
},
point_scale=1.0,
max_point_size=10,
shader="smoothCircle",
colormap="Set1",
has_legend=True,
categorical=True,
legend_labels=legend_labels,
legend_title="Cell Types",
)
faerun.add_tree("CYTO_tree", {
"from": s,
"to": t
},
point_helper="CYTO",
color="#222222")
faerun.plot("cyto")
import flowkit as fk
from bokeh.plotting import show
fcs_file_path = "test_comp_example.fcs"
comp_file_path = "comp_complete_example.csv"
sample = fk.Sample(fcs_path_or_data=fcs_file_path,
compensation=comp_file_path,
subsample_count=50000,
filter_negative_scatter=True,
filter_anomalous_events=False)
xform = fk.transforms.LogicleTransform('logicle',
param_t=262144,
param_w=0.5,
param_m=4.5,
param_a=0)
sample.apply_transform(xform)
fig = sample.plot_scatter(3, 6, source='xform', subsample=True)
show(fig)
try:
files = sorted(list(fcs_dir.glob(f"{_id}*{panel}*.fcs")))
fcs_file = files[0]
# ^^ this will get the most recent in case the are copies (*(1) files)
except IndexError:
try:
fff = list(fcs_dir.glob(f"{_id}x{panel}*.fcs"))
# assert len(fff) in [0, 1]
fcs_file = fff[0]
except IndexError:
print(f"Sample {sample_id} is missing!")
failures.append((panel, sample_id))
continue
try:
s = fk.Sample(fcs_file)
# this shouldn't happen anymore as correupt files aren't selected anymore
except KeyboardInterrupt:
raise
except struct.error:
print(f"Sample {sample_id} failed parsing FCS file!")
failures.append((panel, sample_id))
continue
dates[panel][sample_id] = s.metadata["date"]
print("Concatenating and writing to disk.")
dates_df = pd.DataFrame(dates).apply(pd.to_datetime)
dates_df.index.name = "sample_id"
dates_df.to_csv(metadata_dir / "facs_dates.csv")
import flowkit as fk
from bokeh.plotting import show
# paths to an FCS file and compensation matrix (saved as a simple CSV file)
fcs_file_path = "data/test_comp_example.fcs"
comp_file_path = "data/comp_complete_example.csv"
# create a Sample instance and give the optional comp matrix
# this file is slightly non-standard with a common off-by-one data offset,
# so we force reading it by setting ignore_offset_error to True.
sample = fk.Sample(
fcs_path_or_data=fcs_file_path,
compensation=comp_file_path,
ignore_offset_error=
True # only needed b/c FCS has off-by-one data offset issue
)
# sub-sample events to 50k for better performance when plotting
# the events are not deleted, and any analysis will be performed on all events.
sample.subsample_events(50000)
# create a LogicleTransform instance (one of many transform types in FlowKit)
xform = fk.transforms.LogicleTransform('logicle',
param_t=262144,
param_w=0.5,
param_m=4.5,
param_a=0)
# apply our transform to the sample
# This will apply post-compensation if a comp matrix has already been loaded.
sample.apply_transform(xform)
# Convert fcs to csv:
base_dir = os.getcwd()
fcs_dir = os.path.join(base_dir, 'fcs')
xform_dir = os.path.join(base_dir, 'csv_xform')
fcs_paths = glob(os.path.join(fcs_dir, '*.fcs'))
xform = fk.transforms.AsinhTransform('my_xform',
param_t=12000,
param_m=4.0,
param_a=0.7)
for f in fcs_paths:
sample = fk.Sample(f)
sample.apply_transform(xform)
new_name = os.path.basename(f).replace('fcs', 'csv')
sample.export_csv(source='xform', filename=new_name, directory=xform_dir)
# Extract Batch Control:
from matplotlib.patches import Rectangle
from matplotlib.path import Path
import pandas as pd
import numpy as np
fs = glob('./csv_xform/*')
for f in fs:
