scDeepCluster_pytorch
scDeepCluster, a model-based deep embedding clustering for Single Cell RNA-seq data.
Reference:
Tian, T., Wan, J., Song, Q., & Wei, Z. (2019). Clustering single-cell RNA-seq data with a model-based deep learning approach. Nature Machine Intelligence, 1(4), 191-198. https://www.nature.com/articles/s42256-019-0037-0.
Requirements:
Scanpy -- 1.7
Pytorch -- 1.8
Usage:
python run_scDeepCluster.py --data_file data.h5 --n_clusters 0
Set data_file to the destination to the data (stored in h5 format, with two components X and Y, where X is the cell by gene count matrix, and Y is the true labels. Y is optional), n_clusters to the number of clusters (0 for automatically estimating by the Louvain algorithm on the pretrained latent features).
python run_scDeepClusterBatch.py --data_file data.h5 --n_clusters 0
This is the script for clustering analysis of datasets with batches (stored in h5 format, with three components X, B and Y, where X is the cell by gene count matrix, B is the one-hot encoded batch IDs, and Y is the true labels. Y is optional). n_clusters to the number of clusters (0 for automatically estimating by the Louvain algorithm on the pretrained latent features).
Parameters:
--n_clusters: number of clusters, if setting as 0, it will be estimated by the Louvain alogrithm on the latent features.
--knn: number of nearest neighbors, which is used in the Louvain algorithm, default = 20.
--resolution: resolution in the Louvain algorith, default = 0.8. Larger value will result to more cluster numbers.
--select_genes: number of selected genes for the analysis, default = 0. Recommending to select top 2000 genes, but it depends on different datasets.
--batch_size: batch size, default = 256.
--data_file: file name of data.
--maxiter: max number of iterations in the clustering stage, default = 2000.
--pretrain_epochs: pretraining iterations, default = 300.
--gamma: coefficient of the clustering loss, default = 1.
--sigma: coefficient of the random Gaussian noise, default = 2.5.
--update_interval: number of iteration to update clustering targets, default = 1.
--tol: tolerance to terminate the clustering stage, which is the delta of predicted labels between two consecutive iterations, default = 0.001.
--final_latent_file: file name to output final latent representations of the autoencoder, default = final_latent_file.txt.
--predict_label_file: file name to output clustering labels, default = pred_labels.txt.