https://github.com/vanvalenlab/hubmap-mean-expression-example
Minimal analysis example of data from cytokit+sprm pipeline output
https://github.com/vanvalenlab/hubmap-mean-expression-example
Last synced: about 1 month ago
JSON representation
Minimal analysis example of data from cytokit+sprm pipeline output
- Host: GitHub
- URL: https://github.com/vanvalenlab/hubmap-mean-expression-example
- Owner: vanvalenlab
- Created: 2023-12-19T00:23:50.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-19T00:26:26.000Z (over 2 years ago)
- Last Synced: 2025-11-13T18:03:46.011Z (8 months ago)
- Language: Python
- Size: 2.93 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Example: Mean marker expression by cell type
This repo contains a minimal example demonstrating how to compute a simple
metric (in this case, mean marker expression) by cell type using the
published results from the CODEX cytokit+sprm pipeline from the HubMAP
data portal.
The analysis requires three inputs:
1. The multiplexed image from the CODEX Cytokit+SPRM pipeline (from globus)
2. The corresponding mask produced by the CODEX Cytokit+SPRM pipeline (also from globus)
3. The cell type predictions
The celltype predictions are not currently publicly available as the model is still
under development.
However, a module has been provided which integrates with the Cytokit+SPRM pipeline
to directly produce celltype predictions. This is availble to consortium members
at .
The CWL workflow referenced above adds one more output to the CODEX Cytokit+SPRM
pipeline: a .csv file called `deepcelltypes_predictions.csv` containing the cell type
predictions with the following structure:
```
mask_index,centroid_x,centroid_y,predicted_celltype
```
## Running
First, create a virtual envrionment and install the dependencies with
`pip install -r requirements.txt`
Next, make sure you have all of the necessary CODEX Cytokit+SPRM data
downloaded from globus for the dataset you are working with.
Finally, make sure you have the celltype predictions. These are computed either
by running the Cytokit+SPRM pipeline with the additional prediction CWL
workflow referenced above, or by unpacking the predictions that I've shared
with consortium members from the preliminary model development.
Modify the `pipeline_output_location`, `celltype_prediction_location`,
and `dataset` values in `mean_expression_example.py` to match the locations
on your system.
Then:
```bash
python mean_expression_example.py
```