update readme

README.md

@@ -38,6 +38,7 @@ In the data.h5 file, cell-by-gene count matrix is stored in "X". For dataset wit
 --lr: learning rate in the Adam optimizer, default = 0.001.<br/>
 --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.<br/>
 --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.<br/>
+--gamma: coefficient of the Cauchy kernel, default = 1. Larger gamma means greater repulsive force between non-neighboring points. Please note that larger gamma values will push points to the boundary of the Poincare ball. If wanting to visualize better, user can choose larger beta values for using larger gamma values. In our experience, the KLD loss value < 10 during training t-SNE loss step will result to nice visualization.
 --prob: dropout probability in encoder and decoder layers, default = 0.<br/>
 --perplexity: perplexity of the t-SNE regularization, default = 30.<br/>
 --final_latent_file: file name to output final latent Poincare representations, default = final_latent.txt.<br/>