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Minimal analysis example of data from cytokit+sprm pipeline output

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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 https://github.com/hubmapconsortium/deepcelltypes-hubmap.

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:

python mean_expression_example.py

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Minimal analysis example of data from cytokit+sprm pipeline output

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