Update README.md

README.md

@@ -1,7 +1,11 @@
 # scDeepCluster_pytorch
 
 
-The pytorch version of scDeepCluster, a model-based deep embedding clustering for Single Cell RNA-seq data. 
+The pytorch version of scDeepCluster, a model-based deep embedding clustering for Single Cell RNA-seq data. <br/>
+
+Comparing to the original Keras version, I introduced two new features:<br/>
+1. The Louvain clustering is implemented after pretraining to allow estimating number of clusters.<br/>
+2. A new script "scDeepClusterBatch" uses conditional autoencoder technic to integrate single-cell data from different batches.<br/>
 
 ## Table of contents
 - [Network diagram](#diagram)
@@ -37,7 +41,7 @@ For single-cell count data from multiple batches:
 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).
+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). Following the idea from scVI paper (https://doi.org/10.1038/s41592-018-0229-2), we use the conditional autoencoder (https://papers.nips.cc/paper_files/paper/2015/hash/8d55a249e6baa5c06772297520da2051-Abstract.html) technic to integrate different batches. n_clusters to the number of clusters (0 for automatically estimating by the Louvain algorithm on the pretrained latent features).
 
 ## <a name="parameters"></a>Parameters