DeepHumanBrain#
The DeepHumanBrain model is a peak regression model fine-tuned to cell type-specific regions for cell types in the whole human brain. The dataset was obtained from Li et al., 2023 (Science).
After pretraining on all consensus peaks, the model was fine-tuned to specific peaks obtained with the filter_regions_on_specificity() function. These sets of regions were then used as input to the model, where 2114bp one-hot encoded DNA sequences were used to per cell type the mean peak accessibility over the center 1000 bp of the peak.
Peak heights were normalized across cell types with the normalize_peaks() function.
The model is a CNN multiclass regression model that uses the chrombpnet() architecture. It has 1024 convolutional filters per layer instead of the default 512..
Details of the data and the model can be found in the original publication.
Citation
Hecker, N., Kempynck, N. et al. Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium. bioRxiv (2024). https://doi.org/10.1101/2024.04.17.589795
Usage#
1import crested
2import keras
3
4# download model
5model_path, output_names = crested.get_model("DeepHumanBrain")
6
7# load model
8model = keras.models.load_model(model_path)
9
10# make predictions
11sequence = "A" * 500
12predictions = crested.tl.predict(sequence, model)
13print(predictions.shape)