DeepGlioma

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DeepGlioma#

The DeepGlioma model is a topic classification model trained on a scATAC-seq dataset of human gliomas obtained from Wang et al., 2019 (Cancer discovery). The dataset comprises 6 patient samples, 4 IDH–wild-type GBMs, 2 IDH-mutant grade II astrocytomas, and 2 IDH-mutant oligodendrogliomas.

Using pycisTopic, binarized topics per region were extracted for 24 target topics, where topics 8/21 and topics 20/25 represent patient-specific and patient-mixed MES-like states, while topics 14/18/19 represent an OPC/NPC-like state.

The model is a CNN multiclass classifier that uses the deeptopic_cnn() architecture.

Details of the data and the model can be found in the original publication.

Citation

Kempynck, N., De Winter, S. et al. CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species. Nature Methods (2026). https://doi.org/10.1038/s41592-026-03057-2

Data source

Wang, L., Babikir, H., Müller, S., et al. The Phenotypes of Proliferating Glioblastoma Cells Reside on a Single Axis of Variation. Cancer Discovery (2019). https://doi.org/10.1158/2159-8290.CD-19-0329

Usage#

 1import crested
 2import keras
 3
 4# download model
 5model_path, output_names = crested.get_model("DeepGlioma")
 6
 7# load model
 8model = crested.utils.load_model(model_path)
 9
10# make predictions
11sequence = "A" * 500
12predictions = crested.tl.predict(sequence, model)
13print(predictions.shape)
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