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Tumor & stroma classification using Random Decision Forests

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Tumor/Stroma classification using Random Decision Forests

Cells are classified into tumor or stroma classes based on antigen expression levels of each cell. Data from 31 different antigens are organized as follows:

CCR3 CD103 ... Class
0.115 0.217 ... stroma
1.216 0.412 ... stroma
0.662 0.326 ... tumor

The following antigens are available:

['CCR3', 'CD103', 'CD11B', 'CD141', 'CD163', 'CD19', 'CD3', 'CD31', 'CD4', 'CD45RA', 'CD56', 'CD62L', 'CD8', 'CXCR3', 'EPCAM', 'FAP', 'FOXP3', 'GFAP', 'GZMB', 'IL10', 'INOS', 'KI67', 'LOX1', 'TRYPTASE', 'MPO', 'MUC1', 'PD1', 'PDL1', 'PRG2', 'SMA', 'VIMENTIN']

A total of 268074 cells were identified from 6 patients on multiplexed whole slide images. Regions were identified as tumor or stroma by pathologists. Fitting random forest models, antigens are ranked per feature importance as reported by the best models. The scripts analyze the following tests on all patients and individual patients:

  • all antigens
  • all antigens but MUC1, KI67, EPCAM
  • all but top K antigens by intensity means
  • only top K antigens found after ranking all antigens

👥 Contributing

Within the Informatics Center, we value the health of our community as much as we do code. As a result, we ask you read through the following:

  • Our contributor's guide tells what kinds of contributions we take.
  • Our code of conduct delineates the standards of behavior we both practice and expect by everyone who participates with this software.

🔢 Versioning

We use the SemVer philosophy for versioning this software.

📃 License

The project is licensed under the Apache version 2 license.

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