Compare Mean Fluorescence Intensity of Gated Events

[1]:

import os
import pandas as pd

import flowkit as fk

%matplotlib inline

Load FlowJo Workspace

[2]:

base_dir = "../../../data/8_color_data_set"

sample_path = os.path.join(base_dir, "fcs_files")
wsp_path = os.path.join(base_dir, "8_color_ICS.wsp")

[3]:

workspace = fk.Workspace(wsp_path, fcs_samples=sample_path)

[4]:

workspace.summary()

[4]:

	samples	loaded_samples	gates	max_gate_depth
group_name
All Samples	3	3	14	6
DEN	3	3	14	6
GEN	0	0	0	0
G69	0	0	0	0
Lyo Cells	0	0	0	0

[5]:

sample_ids = workspace.get_sample_ids('DEN')

[6]:

sample_ids

[6]:

['101_DEN084Y5_15_E01_008_clean.fcs',
 '101_DEN084Y5_15_E03_009_clean.fcs',
 '101_DEN084Y5_15_E05_010_clean.fcs']

[7]:

# choose a sample ID
sample_id = '101_DEN084Y5_15_E03_009_clean.fcs'
sample = workspace.get_sample(sample_id)

Review the gate hierachy

[8]:

print(workspace.get_gate_hierarchy(sample_id, 'ascii'))

root
╰── Time
    ╰── Singlets
        ╰── aAmine-
            ╰── CD3+
                ├── CD4+
                │   ├── CD107a+
                │   ├── IFNg+
                │   ├── IL2+
                │   ╰── TNFa+
                ╰── CD8+
                    ├── CD107a+
                    ├── IFNg+
                    ├── IL2+
                    ╰── TNFa+

Process gates for the sample group

[9]:

workspace.analyze_samples(sample_id=sample_id)

Get gated events for a sample

[10]:

cd4_gate_name = 'CD4+'
cd4_events = workspace.get_gate_events(sample_id, cd4_gate_name)

[11]:

sample.event_count, cd4_events.shape[0]

[11]:

(283969, 81855)

[12]:

cd4_events.head(5)

[12]:

	sample_id	FSC-A	FSC-H	FSC-W	SSC-A	SSC-H	SSC-W	TNFa FITC FLR-A	CD8 PerCP-Cy55 FLR-A	IL2 BV421 FLR-A	Aqua Amine FLR-A	IFNg APC FLR-A	CD3 APC-H7 FLR-A	CD107a PE FLR-A	CD4 PE-Cy7 FLR-A	Time
1	101_DEN084Y5_15_E03_009_clean.fcs	0.484332	0.397831	0.304358	0.178974	0.165913	0.269681	0.242188	0.230791	0.282167	0.254901	0.244743	0.454572	0.276832	0.610560	0.029446
6	101_DEN084Y5_15_E03_009_clean.fcs	0.858325	0.713455	0.300763	0.160702	0.149963	0.267902	0.255704	0.297297	0.270034	0.259815	0.232809	0.383849	0.272156	0.476992	0.029638
7	101_DEN084Y5_15_E03_009_clean.fcs	0.499515	0.389256	0.320814	0.160819	0.148792	0.270207	0.542186	0.298411	0.430466	0.270496	0.720327	0.324062	0.353662	0.604535	0.029652
9	101_DEN084Y5_15_E03_009_clean.fcs	0.427556	0.346325	0.308638	0.181880	0.169140	0.268832	0.256053	0.305138	0.332638	0.251144	0.226288	0.607199	0.369072	0.423495	0.029680
16	101_DEN084Y5_15_E03_009_clean.fcs	0.527936	0.415756	0.317456	0.103247	0.096481	0.267532	0.241784	0.235227	0.283300	0.246906	0.251326	0.454224	0.306893	0.487684	0.029858

Calculate means for channels of interest

[13]:

fluoro_labels = []
for chan_idx in sample.fluoro_indices:
    fluoro_labels.append(sample.pnn_labels[chan_idx])

[14]:

cd4_events = cd4_events[fluoro_labels]

[15]:

cd4_mfi = cd4_events.mean()

[16]:

cd4_mfi

[16]:

TNFa FITC FLR-A         0.248338
CD8 PerCP-Cy55 FLR-A    0.274010
IL2 BV421 FLR-A         0.288348
Aqua Amine FLR-A        0.250962
IFNg APC FLR-A          0.246429
CD3 APC-H7 FLR-A        0.488177
CD107a PE FLR-A         0.279935
CD4 PE-Cy7 FLR-A        0.588879
dtype: float64

Let’s compare MFI between CD4+ & CD8+

[17]:

cd8_gate_name = 'CD8+'
cd8_events = workspace.get_gate_events(sample_id, cd8_gate_name)

cd8_mfi = cd8_events[fluoro_labels].mean()

[18]:

mfi_comp = pd.DataFrame([cd4_mfi, cd8_mfi], index=[cd4_gate_name, cd8_gate_name])

[19]:

mfi_comp

[19]:

	TNFa FITC FLR-A	CD8 PerCP-Cy55 FLR-A	IL2 BV421 FLR-A	Aqua Amine FLR-A	IFNg APC FLR-A	CD3 APC-H7 FLR-A	CD107a PE FLR-A	CD4 PE-Cy7 FLR-A
CD4+	0.248338	0.274010	0.288348	0.250962	0.246429	0.488177	0.279935	0.588879
CD8+	0.250975	0.584248	0.283504	0.251208	0.252061	0.459689	0.292997	0.308986

[20]:

mfi_comp = mfi_comp.transpose()

[21]:

mfi_comp

[21]:

	CD4+	CD8+
TNFa FITC FLR-A	0.248338	0.250975
CD8 PerCP-Cy55 FLR-A	0.274010	0.584248
IL2 BV421 FLR-A	0.288348	0.283504
Aqua Amine FLR-A	0.250962	0.251208
IFNg APC FLR-A	0.246429	0.252061
CD3 APC-H7 FLR-A	0.488177	0.459689
CD107a PE FLR-A	0.279935	0.292997
CD4 PE-Cy7 FLR-A	0.588879	0.308986

[22]:

ax = mfi_comp.plot.bar(figsize=(12, 6))
ax.set_title("Mean Fluorescence Intensity - CD4+ vs CD8+", color='black', fontsize=18)
ax.legend(bbox_to_anchor=(1.0, 1.0), fontsize=12)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ax.plot()

[22]:

[]

../../_images/notebooks_advanced_compare-mfi-of-gated-events_28_1.png

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