Understanding the developmental process is a critical step in single-cell analysis. This repo proposes scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of single-cell sequencing data in a low dimensional hyperbolic space. ScDHMap can be used for various dimensionality reduction tasks including revealing trajectory branches, batch correction, and denoising highly dropout counts.
Requirements
Python: 3.9.5
PyTorch: 1.9.1
Scanpy: 1.7.2
Numpy: 1.21.2
sklearn: 0.24.2
Scipy: 1.6.3
Pandas: 1.2.5
h5py: 3.2.1
Optional: harmonypy (https://github.com/slowkow/harmonypy)
Usage
python run_scDHMap.py --data_file data.h5
python run_scDHMap_batch.py --data_file data.h5
The real single cell datasets used in this study can be found: https://figshare.com/s/64694120e3d2b87e21c3
In the data.h5 file, cell-by-gene count matrix is stored in "X". For dataset with batches, batch IDs are one-hot encoded matrix and stored in "Y".
Parameters
--batch_size: batch size, default = 512.
--data_file: data file name.
--select_genes: number of selected genes for embedding analysis, default = 1000.
--n_PCA: number of principle components for the t-SNE part, default = 50.
--pretrain_iter: number of pretraining iterations, default = 400.
--maxiter: number of max iterations during training stage, default = 5000.
--patience: patience in training stage, default = 150.
--lr: learning rate in the Adam optimizer, default = 0.001.
--alpha: coefficient of the t-SNE regularization, default = 1000. The choice of alpha is to balance the number of genes in the ZINB reconstruction loss.
--beta: coefficient of the wrapped normal KLD loss, default = 10. If points in the embedding are all stacked near the boundary of the Poincare disk, you may choose a larger beta value.
--prob: dropout probability in encoder and decoder layers, default = 0.
--perplexity: perplexity of the t-SNE regularization, default = 30.
--final_latent_file: file name to output final latent Poincare representations, default = final_latent.txt.
--final_mean_file: file name to output denoised counts, default = denoised_mean.txt.