update leiden

examples/10X_P50/README.md

@@ -379,18 +379,30 @@ Using selected significant PCs, we next construct a K Nearest Neighbor (KNN) Gra
 ```
 
 **Step 11. Clustering (SnapATAC)**                  
-Using the resulting KNN graph, we next apply community finding algorithm Louvain to identify the clusters which represent groups of cells sharing similar ATAC-seq profiles, potentially originating from the same cell type. Two Louvain methods are included, one is using the `R-igraph` package and the other applies a `pyhon-louvain` implementation. `R-igraph` is faster but does not support different resolution. `pyhon-louvain` is slower and requires ` snaptools` but it does allows for multiple resolutions.
+Using the resulting KNN graph, we next apply community finding algorithm Louvain/leiden to identify the clusters which represent groups of cells sharing similar ATAC-seq profiles, potentially originating from the same cell type. Two methods are included here, one is using `cluster_louvain` in the `R-igraph` package and the other applies a `leiden`. 
 
 ```R
 > x.sp = runCluster(
 	obj=x.sp,
 	tmp.folder=tempdir(),
 	louvain.lib="R-igraph",
-	path.to.snaptools=NULL,
 	seed.use=10
 	);
 ```
 
+Alternative, we can also use leiden for clustering which allows for choosing different resolution resulting different clustering results. It requires R package `leiden` to be pre-installed but highly recommended.
+
+```R
+> library(leiden);
+> x.sp = runCluster(
+	obj=x.sp,
+	tmp.folder=tempdir(),
+	louvain.lib="leiden",
+	seed.use=10,
+	resolution=1
+	);
+```
+
 **Step 12. Non-linear dimentionality reduction (SnapATAC)**         
 SnapATAC visualize the datausing  tSNE, UMAP and FIt-sne. In the following example, data is visulized by tsne implemented by R package (Rtsne). To run `umap`, you need to first install umap package. To run `fast_tsne`, you need to first install [fast_tsne package](https://github.com/KlugerLab/FIt-SNE/blob/master/fast_tsne.R).
 

examples/Fang_2019/Fang_2019.md

@@ -258,11 +258,23 @@ Using the resulting KNN graph, we next apply community finding algorithm Louvain
 	obj=x.sp,
 	tmp.folder=tempdir(),
 	louvain.lib="R-igraph",
-	path.to.snaptools=NULL,
 	seed.use=10
 	);
 ```
 
+Alternative, we can also use leiden for clustering which allows for choosing different resolution resulting different clustering results. It requires R package `leiden` to be pre-installed but highly recommended.
+
+```R
+> library(leiden);
+> x.sp = runCluster(
+	obj=x.sp,
+	tmp.folder=tempdir(),
+	louvain.lib="leiden",
+	seed.use=10,
+	resolution=1
+	);
+```
+
 **Step 12. Non-linear dimentionality reduction (SnapATAC)**         
 SnapATAC visualize the datausing  tSNE, UMAP and FIt-sne. In the following example, data is visulized by tsne implemented by R package (Rtsne). To run `umap`, you need to first install umap package. To run `fast_tsne`, you need to first install [fast_tsne package](https://github.com/KlugerLab/FIt-SNE/blob/master/fast_tsne.R).