Flow cytometry dataset visualization demo, using Jupyter, Plotly, and fcsparser.
To run this quickly, you may view the Jupyter notebook in nbviewer:
It can be useful to run this locally, to make tweaks and see how things work.
First, install required Python libraries:
$ pip install fcsparser
$ pip install jupyter
$ pip install plotly
$ pip install numpy
Installing these libraries could take a few minutes.
The fcsparser library is used to open and process FCS files. The other libraries are used for processing and visualizing the data.
Open the notebook file FlowCytometryVisualization.ipynb:
$ jupyter notebook FlowCytometryVisualization.ipynb
This will open the notebook in your default web browser.
Make a couple adjustments, as needed, to load local data and select desired datasets:
- Adjust the value of the
pathvariable to point to a locally-saved copy of your FCS file. The desired FCS file could be put into the same directory as this notebook. - Adjust the desired column names in the
colsOfInterest; these are used to render the three axes of the scatterplot.
After making adjustments, re-run the Jupyter notebook. To do this, pull down the Kernel menu and select Restart & Run All.
The scatterplot is available at the bottom of the notebook and can be interacted with click-and-drag and scrollwheel actions, which rotate and zoom the plot.
A PNG file of the scatterplot can be exported by clicking on the camera icon in the top-right corner of the scatterplot window.