change the link of datasets

.gitignore

@@ -2,4 +2,5 @@
 .Rhistory
 .RData
 .Rproj
+.idea
 test/*

examples/10X_PBMC_15K/README.md

@@ -2,8 +2,8 @@
 
 In this example, we will be analyzing two scATAC-seq datasets (5K and 10K) and one scRNA-seq dataset from PBMC. All three datasets are freely available from 10X genomics. All the data used in this study can be downloaded [here](http://renlab.sdsc.edu/r3fang//share/github/PBMC_ATAC_RNA/).
 
-In detail, we will be performing the following analysis:
-
+In detail, we will be performing the following analysis:
+
 1.	Cell selection for PBMC 5k and 10k scATAC;
 2. Randomly sample 10,000 cells as landmarks;
 3. Unsupervised clustering of landmarks;
@@ -34,16 +34,23 @@ In detail, we will be performing the following analysis:
 - [Step 18. Predict gene-enhancer pairs](#gene_peak_pair)
 
 <a name="data_download"></a>**Step 0. Data Download**          
-We will start from quality control file `singlecell.csv` generated by cell-ranger ATAC pipeline and `snap` file generated using snaptools. See [here](https://github.com/r3fang/SnapATAC/wiki/FAQs#bam_snap) about how to create a snap file.
+We will start from quality control file `singlecell.csv` generated by cell-ranger ATAC pipeline and `snap` file generated using snaptools. See [here](https://github.com/r3fang/SnapATAC/wiki/FAQs#bam_snap) about how to create a snap file. We will download the snap file(See [here](https://drive.google.com/drive/folders/1nuBmpcV6Gth4GEufxIgwPOb1qUULoMeM?usp=sharing)).
 
-```bash
-$ wget http://renlab.sdsc.edu/r3fang//share/github/PBMC_ATAC_RNA/atac_pbmc_5k_nextgem.snap
-$ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_5k_nextgem/atac_pbmc_5k_nextgem_singlecell.csv
-$ wget http://renlab.sdsc.edu/r3fang//share/github/PBMC_ATAC_RNA/atac_pbmc_10k_nextgem.snap
-$ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_singlecell.csv
-$ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_10k_nextgem/hg19.blacklist.bed.gz
-$ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/gencode.v19.annotation.gene.bed
-```
+[comment]: <> (```bash)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang//share/github/PBMC_ATAC_RNA/atac_pbmc_5k_nextgem.snap)
+
+[comment]: <> ($ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_5k_nextgem/atac_pbmc_5k_nextgem_singlecell.csv)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang//share/github/PBMC_ATAC_RNA/atac_pbmc_10k_nextgem.snap)
+
+[comment]: <> ($ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_singlecell.csv)
+
+[comment]: <> ($ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_pbmc_10k_nextgem/hg19.blacklist.bed.gz)
+
+[comment]: <> ($ wget http://cf.10xgenomics.com/samples/cell-atac/1.1.0/gencode.v19.annotation.gene.bed)
+
+[comment]: <> (```)
 
 <a name="barcode_selection"></a>**Step 1. Barcode selection**                 
 First, we select the high-quality barcodes based on two major criteria: 1) number of unique fragments; 2) fragments in promoter ratio; 
@@ -216,7 +223,7 @@ First, we filter out any bins overlapping with the [ENCODE blacklist](http://mit
 ## number of peaks: 0
 ## number of motifs: 0
 ```
-Second, we remove unwanted chromosomes.
+Second, we remove unwanted chromosomes.
 
 ```R
 > chr.exclude = seqlevels(x.sp@feature)[grep("random|chrM", seqlevels(x.sp@feature))];
@@ -284,7 +291,7 @@ A Nyström landmark diffusion maps algorithm includes three major steps:
 
 1. ***_sampling_***: sample a subset of K (K≪N) cells from N total cells as “landmarks”. Instead of random sampling, here we adopted a density-based sampling approach developed in SCTransform to preserve the density distribution of the N original points;
 2. ***_embedding_***: compute a diffusion map embedding for K landmarks;
-3. ***_extension_***: project the remaining N-K cells onto the low-dimensional embedding as learned from the landmarks to create a joint embedding space for all cells. 
+3. ***_extension_***: project the remaining N-K cells onto the low-dimensional embedding as learned from the landmarks to create a joint embedding space for all cells. 
 
 In this example, we will sample 10,000 cells as landmarks and project the remaining query cells onto the diffusion maps embedding of landmarks.
 

examples/10X_brain_5k/README.md

@@ -24,12 +24,15 @@ In this example, we will be analyzing a dataset of 5K cells from the adult mouse
 
 
 <a name="data_download"></a>**Step 0. Data download**        
-In this example, we will skip the snap generation (See [here](https://github.com/r3fang/SnapATAC/blob/master/examples/10X_PBMC_15K/README.md) for how to generate a snap file). Instead, we will download the snap file. The downloaded snap file already contains the cell-by-bin/cell-by-peak matrix.
+In this example, we will skip the snap generation (See [here](https://github.com/r3fang/SnapATAC/blob/master/examples/10X_PBMC_15K/README.md) for how to generate a snap file). Instead, we will download the snap file(See [here](https://drive.google.com/drive/folders/1xPqPtVXtWjOwZJynQhGalNrjEwKmxFhc?usp=sharing)). The downloaded snap file already contains the cell-by-bin/cell-by-peak matrix.
 
-```bash
-$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X/atac_v1_adult_brain_fresh_5k.snap
-$ http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X/atac_v1_adult_brain_fresh_5k_singlecell.csv
-```
+[comment]: <> (```bash)
+
+[comment]: <> (#$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X/atac_v1_adult_brain_fresh_5k.snap)
+
+[comment]: <> (#$ http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X/atac_v1_adult_brain_fresh_5k_singlecell.csv)
+
+[comment]: <> (```)
 
 <a name="barcode_selection"></a>**Step 1. Barcode selection**     
 We select high-quality barcodes based on two criteria: 1) number of unique fragments; 2) fragments in promoter ratio; 
@@ -276,7 +279,7 @@ SnapATAC visualizes and explores the data using tSNE (FI-tsne) or UMAP. In this
 To help annotate identified cell clusters, SnapATAC next creates the cell-by-gene matrix and visualize the enrichment of marker genes.
 
 ```R
-> system("wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X/gencode.vM16.gene.bed");
+# get the gencode.vM16.gene.bed from the dataset download link.
 > genes = read.table("gencode.vM16.gene.bed");
 > genes.gr = GRanges(genes[,1], 
     IRanges(genes[,2], genes[,3]), name=genes[,4]

examples/10X_snATAC/README.md

@@ -21,12 +21,19 @@ In this example, we will be integrating two datasets from adult mouse brain gene
 
 <a name="download_data"></a>**Step 0. Download data**
 
-```
-$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/CEMBA180305_2B.snap
-$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/CEMBA180305_2B.barcode.txt
-$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/atac_v1_adult_brain_fresh_5k.snap	
-$ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/atac_v1_adult_brain_fresh_5k.barcode.txt
-```
+We will download the snap file(See [here](https://drive.google.com/drive/folders/1yI2DNLxRhyAJJt8ogkDZs1uBKAUyy5gt?usp=sharing)).
+
+[comment]: <> (```)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/CEMBA180305_2B.snap)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/CEMBA180305_2B.barcode.txt)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/atac_v1_adult_brain_fresh_5k.snap	)
+
+[comment]: <> ($ wget http://renlab.sdsc.edu/r3fang/share/github/Mouse_Brain_10X_snATAC/atac_v1_adult_brain_fresh_5k.barcode.txt)
+
+[comment]: <> (```)
 
 <a name="create_snap"></a>**Step 1. Create snap object**       
 In this example, we will create a list of snap objects that contains two datasets.