Flow Cytometry Data Analysis: Reading FCS Files and Applying t-SNE with Python

To read flow cytometry FCS files with Python and apply t-SNE to the data, you can use the 'FlowCytometryTools' library. Here's an example code that demonstrates the process:

1. Install the required libraries:

pip install FlowCytometryTools
pip install sklearn

2. Import the necessary libraries:

import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from FlowCytometryTools import FCMeasurement

3. Load the FCS file:

filename = 'path/to/your/fcs/file.fcs'
sample = FCMeasurement(ID='Test Sample', datafile=filename)

4. Extract the relevant data:

# Extract the fluorescence channels you are interested in
channels = ['FL1-Log', 'FL2-Log', 'FL3-Log']

# Extract the data and convert it to a numpy array
data = sample.data[channels].values

5. Apply t-SNE to the data:

# Create a t-SNE object
tsne = TSNE(n_components=2, random_state=0)

# Perform t-SNE on the data
tsne_data = tsne.fit_transform(data)

6. Plot the t-SNE visualization:

# Create a scatter plot of the t-SNE results
plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c=sample['Time'], cmap='viridis')
plt.colorbar()

# Add labels and title
plt.xlabel('t-SNE Dimension 1')
plt.ylabel('t-SNE Dimension 2')
plt.title('t-SNE Visualization')

# Show the plot
plt.show()

Make sure to replace 'path/to/your/fcs/file.fcs' with the actual path to your FCS file. The code will load the FCS file, extract the desired fluorescence channels, apply t-SNE to the data, and then plot the t-SNE visualization.

Note: The example assumes three fluorescence channels ('FL1-Log', 'FL2-Log', 'FL3-Log'). You may need to modify the code to match the specific fluorescence channels in your FCS file.