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+Compiled date: 25th Feb
+
+Source: [Tutorials/README.md](https://github.com/MengQiuchen/cPeaks/blob/dev-tutorials2/Tutorials/README.md)
+
+
+# cPeak Tutorials
+
+## What is cPeak?
+
+cPeak is xxxxxxx. (introduction and benefit) xxx
+xxxxx
+xxxxx
+xxxxx
+
+### Download Files
+
+cPeak reference files are available from: [hg19 file](https://cloud.tsinghua.edu.cn/f/d9f40ed01cf749478080/?dl=1) and [hg38 file](https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1). Or downloaded by:
+```bash
+wget -O YOUR_PATH/cpeaks_hg19_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+wget -O YOUR_PATH/cpeak_hg38_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+```
+## Usages in a Nutshell
+
+
+
+
+Using cPeak is to replace call peaking step by using cPeak reference. If you are familiar with snapATAC2 or ArchR or python, this part we will help you get started with cPeak very quickly. Otherwise if you get confused about anything in this part, please refer to [detailed tutorials](#detail).
+
+- snapATAC2
+ ```python
+ # read cPeak file
+ cpeaks = open('YOUR_PATH/cpeaks_hg19_features.txt').read().strip().split()
+ # set parameter use_rep as
+ data = snapatac2.pp.make_peak_matrix(data, use_rep=cpeaks)
+ ```
+- ArchR
+ ```r
+ cpeaks <- read_table('YOUR_PATH/cpeaks.v3.simple.bed',col_names=F)
+ cpeaks.gr <- GRanges(seqnames=cpeaks$X1, ranges=IRanges(cpeaks$X2, cpeaks$X3))
+ proj <- addFeatureMatrix(proj,features = gr,matrixName='FeatureMatrix')
+ ```
+- Run Python Script Manually
+ ```bash
+ git clone https://github.com/MengQiuchen/cPeaks.git
+ cd cPeaks/map2cpeaks
+ python main.py --fragment_path PATH/to/YOUR_fragment.tsv.gz --output map2cpeaks_result --output_name Cell_by_cPeak_Matrix --type_saved .mtx
+ ```
+ [See more about paramters for main.py](#param).
+
+
+## Usages in Detail
+
+You can use cPeak
+snapATAC2 is a widely used python package for ATAC data analysis. ArchR is a widely used R package for ATAC data analysis. Either can be used for ATAC data analysis.
+
+* [snapATAC2](#method2): snapATAC2 is a widely used python package for ATAC data analysis. Click the [link](https://github.com/kaizhang/SnapATAC2) for detailed information.
+* [ArchR](#method3): ArchR is a widely used R package for ATAC data analysis.
+* [Direct Use](#method1): Use python script [main.py](https://github.com/MengQiuchen/cPeaks/blob/main/main.py) to transform fragment file to cPeak-based data matrix. It can be use to downstream analysis steps.
+
+
+
+## snapATAC2
+
+### 1.Download cPeak
+
+```bash
+wget -O cpeaks_hg19_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+wget -O cpeak_hg38_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+```
+
+### 2.Install snapATAC2
+
+https://kzhang.org/SnapATAC2/install.html
+
+### 3.Analyze cPeak with snapATAC2
+
+To start, we need to download a fragment file. This can be achieved from https://cloud.tsinghua.edu.cn/d/7f2ccf8067314a4b9a02/
+```python
+import snapatac2 as snap
+
+data = snap.pp.import_data(
+ fragment_paths,
+ chrom_sizes=snap.genome.hg19,
+ sorted_by_barcode=False,
+ n_jobs=90
+ )
+
+snap.metrics.tsse(data, snap.genome.hg38)
+data = snap.pp.make_peak_matrix(data,use_rep=gr)
+snap.pp.select_features(data,n_features=len(gr)) # kan renbin
+snap.tl.spectral(data,n_comps=30)
+snap.tl.umap(data)
+snap.pp.knn(data)
+snap.tl.leiden(data,resolution=0.5)
+snap.pl.umap(data, color='leiden', interactive=False, height=500)
+```
+
+The resulting plot is as follows:
+
+
+
+
+
+Detailed methods refer to https://kzhang.org/SnapATAC2/tutorials/pbmc.html
+
+## ArchR
+
+### 1.Download cPeak
+
+```bash
+wget -O cpeaks_hg19_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+wget -O cpeak_hg38_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+```
+
+### 2. Install ArchR
+
+ https://www.archrproject.com/
+
+### 3. Analyze cPeak with ArchR
+
+Code references to https://www.archrproject.com/bookdown/index.html
+
+```r
+library(ArchR)
+set.seed(1)
+addArchRThreads(threads = 90)
+library(GenomicRanges)
+library(tidyverse)
+library(dplyr)
+addArchRGenome(‘hg19')
+
+cpeaks = read_table('/nfs/mqc/Consensus_peak/code/cpeaks/all/hg19/cpeaks.v3.simple.bed',col_names = F)
+cpeaks.gr <- GRanges(seqnames = cpeaks$X1,
+ ranges = IRanges(cpeaks$X2, cpeaks$X3))
+get_cluster = function(fragement_path,output.name,num_cluster,gr=NULL,ref = 'hg19'){
+ addArchRGenome(ref)
+ if (length(list.files(fragement_path)) == 0 & (!endsWith(fragement_path,'tsv.gz'))){
+ print("input should be tsv.gz or list of tsv.gz")
+ return()
+ }
+ if (length(list.files(fragement_path))>0){
+ file_list <- list.files(fragement_path, pattern = "fragments.tsv.gz$", full.names = TRUE)
+ print(paste0('there are ',length(file_list),' fragments files'))
+ file_name <- lapply(file_list, function(x) {
+ tmp <- strsplit(x, '/')[[1]]
+ tmp[length(tmp)]
+ })
+ }
+ else{
+ file_list = fragement_path
+ print(paste0('there are 1 files: ',fragement_path))
+ tmp <- strsplit(fragement_path, '/')[[1]]
+ file_name = tmp[length(tmp)]
+ }
+ ArrowFiles <- createArrowFiles(
+ inputFiles = file_list,
+ sampleNames = file_name,
+ minTSS = 0, #Dont set this too high because you can always increase later
+ minFrags = 0,
+ addTileMat = TRUE,
+ addGeneScoreMat = TRUE
+ )
+ proj = ArchRProject(
+ ArrowFiles = ArrowFiles,
+ outputDirectory = output.name,
+ copyArrows = F #This is recommened so that you maintain an unaltered copy for later usage.
+ )
+ #proj <- filterDoublets(ArchRProj = proj)
+ if (!is.null(gr)){
+ proj = addFeatureMatrix(proj,features = gr,matrixName='FeatureMatrix')
+ proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "FeatureMatrix", name = "IterativeLSI")
+ }
+ else{
+ proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "TileMatrix", name = "IterativeLSI")
+ }
+ proj <- addClusters(input = proj, reducedDims = "IterativeLSI",maxClusters=num_cluster)
+ proj <- addUMAP(ArchRProj = proj, reducedDims = "IterativeLSI")
+ p1 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Sample", embedding = "UMAP")
+ p2 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Clusters", embedding = "UMAP")
+ ggAlignPlots(p1, p2, type = "h")
+ rname = row.names(getCellColData(proj))
+ cluster = unlist(proj$Clusters%>%lapply(function(x){strsplit(x,'C')[[1]][2]%>%as.numeric}))
+ pre = cbind(rname,cluster)%>%as.data.frame
+ pre$cluster = as.numeric(pre$cluster)
+ #saveArchRProject(ArchRProj = proj, outputDirectory = paste0(output.name,'_save'), load = FALSE)
+ return(pre)
+}
+library(parallel)
+res = get_cluster("sort.HSC.fragments.tsv.gz",'HSC_all',num_cluster = 10,gr=cpeaks.gr)
+
+```
+
+## Run Python Script Manually
+
+### main.py Parameters
+
+| Parameter | Default | Description |
+| ------ | ------ | ------ |
+| fragment_path | - | The input file must be *.tsv.gz file, which will be transformed to cPeak reference. |
+| barcode_path | - | If barcode file is given, barcode in the file or all barcodes in the fragment will be used. |
+| reference | hg38 | cPeak version, hg38 or hg19. |
+| type_saved | .mtx | The type of output file, .mtx or .h5ad. |
+| output | map2cpeaks_result | Output folder name. |
+| output_name | cell-cpeaks | Name of output files. |
+| num_cores | 10 | Number of cores to use. |
+
+
+### Version
+Python (>=3.7)
+
+### Requirments
+
+```
+numpy
+gzip
+tqdm
+```
+
+### Method 1: Map the sequencing reads (fragments.tsv.gz) in each sample/cell to generate cell-by-cPeak matrix (.mtx/.h5ad)
+
+Map the sequencing reads (fragments.tsv.gz) in each sample/cell to generate cell-by-cPeak matrix (.mtx/.h5ad)
+```bash
+usage:
+
+
+The output file contains a barcode.txt and an mtx file that stores the matrix of map results.
+
+### Method 2. Directly map the pre-identified features like peaks to cPeaks (NOT recommand)
+
+**This is not a good idea.** It may lose information in the genomic regions which are not included in pre-identfied features. Also, for bulk ATAC-seq data, the quantification of each cPeak is inaccurate.
+
+```bash
+usage: python main.py [--bed_path feature.bed]
+
+--bed_path, -bed: the input feature.bed file, for example, MACS2calledPeaks.bed.
+```
+
+
diff --git a/Tutorials/_sidebar.md b/Tutorials/_sidebar.md
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+* Test
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diff --git a/Tutorials/index.html b/Tutorials/index.html
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+
+
+
+
+ Document
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Using cPeak is to replace call peaking step by using cPeak reference. If you are familiar with snapATAC2 or ArchR or python, this part we will help you get started with cPeak very quickly. Otherwise if you get confused about anything in this part, please refer to [detailed tutorials](#detail).
+
+- snapATAC2
+ ```python
+ # read cPeak file
+ cpeaks = open('YOUR_PATH/cpeaks_hg19_features.txt').read().strip().split()
+ # set parameter use_rep as
+ data = snapatac2.pp.make_peak_matrix(data, use_rep=cpeaks)
+ ```
+- ArchR
+ ```r
+ cpeaks <- read_table('YOUR_PATH/cpeaks.v3.simple.bed',col_names=F)
+ cpeaks.gr <- GRanges(seqnames=cpeaks$X1, ranges=IRanges(cpeaks$X2, cpeaks$X3))
+ proj <- addFeatureMatrix(proj,features = gr,matrixName='FeatureMatrix')
+ ```
+- Run Python Script Manually
+ ```bash
+ git clone https://github.com/MengQiuchen/cPeaks.git
+ cd cPeaks/map2cpeaks
+ python main.py --fragment_path PATH/to/YOUR_fragment.tsv.gz --output map2cpeaks_result --output_name Cell_by_cPeak_Matrix --type_saved .mtx
+ ```
+ [See more about paramters for main.py](#param).
+
+
+## Usages in Detail
+
+You can use cPeak
+snapATAC2 is a widely used python package for ATAC data analysis. ArchR is a widely used R package for ATAC data analysis. Either can be used for ATAC data analysis.
+
+* [snapATAC2](#method2): snapATAC2 is a widely used python package for ATAC data analysis. Click the [link](https://github.com/kaizhang/SnapATAC2) for detailed information.
+* [ArchR](#method3): ArchR is a widely used R package for ATAC data analysis.
+* [Direct Use](#method1): Use python script [main.py](https://github.com/MengQiuchen/cPeaks/blob/main/main.py) to transform fragment file to cPeak-based data matrix. It can be use to downstream analysis steps.
+
+
+
+## snapATAC2
+
+### 1.Download cPeak
+
+```bash
+wget -O cpeaks_hg19_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+wget -O cpeak_hg38_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+```
+
+### 2.Install snapATAC2
+
+https://kzhang.org/SnapATAC2/install.html
+
+### 3.Analyze cPeak with snapATAC2
+
+To start, we need to download a fragment file. This can be achieved from https://cloud.tsinghua.edu.cn/d/7f2ccf8067314a4b9a02/
+```python
+import snapatac2 as snap
+
+data = snap.pp.import_data(
+ fragment_paths,
+ chrom_sizes=snap.genome.hg19,
+ sorted_by_barcode=False,
+ n_jobs=90
+ )
+
+snap.metrics.tsse(data, snap.genome.hg38)
+data = snap.pp.make_peak_matrix(data,use_rep=gr)
+snap.pp.select_features(data,n_features=len(gr)) # kan renbin
+snap.tl.spectral(data,n_comps=30)
+snap.tl.umap(data)
+snap.pp.knn(data)
+snap.tl.leiden(data,resolution=0.5)
+snap.pl.umap(data, color='leiden', interactive=False, height=500)
+```
+
+The resulting plot is as follows:
+
+
+
+
+
+Detailed methods refer to https://kzhang.org/SnapATAC2/tutorials/pbmc.html
+
+## ArchR
+
+### 1.Download cPeak
+
+```bash
+wget -O cpeaks_hg19_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+wget -O cpeak_hg38_features.txt 'https://cloud.tsinghua.edu.cn/f/dc1c89903e8744eea0aa/?dl=1'
+```
+
+### 2. Install ArchR
+
+ https://www.archrproject.com/
+
+### 3. Analyze cPeak with ArchR
+
+Code references to https://www.archrproject.com/bookdown/index.html
+
+```r
+library(ArchR)
+set.seed(1)
+addArchRThreads(threads = 90)
+library(GenomicRanges)
+library(tidyverse)
+library(dplyr)
+addArchRGenome(‘hg19')
+
+cpeaks = read_table('/nfs/mqc/Consensus_peak/code/cpeaks/all/hg19/cpeaks.v3.simple.bed',col_names = F)
+cpeaks.gr <- GRanges(seqnames = cpeaks$X1,
+ ranges = IRanges(cpeaks$X2, cpeaks$X3))
+get_cluster = function(fragement_path,output.name,num_cluster,gr=NULL,ref = 'hg19'){
+ addArchRGenome(ref)
+ if (length(list.files(fragement_path)) == 0 & (!endsWith(fragement_path,'tsv.gz'))){
+ print("input should be tsv.gz or list of tsv.gz")
+ return()
+ }
+ if (length(list.files(fragement_path))>0){
+ file_list <- list.files(fragement_path, pattern = "fragments.tsv.gz$", full.names = TRUE)
+ print(paste0('there are ',length(file_list),' fragments files'))
+ file_name <- lapply(file_list, function(x) {
+ tmp <- strsplit(x, '/')[[1]]
+ tmp[length(tmp)]
+ })
+ }
+ else{
+ file_list = fragement_path
+ print(paste0('there are 1 files: ',fragement_path))
+ tmp <- strsplit(fragement_path, '/')[[1]]
+ file_name = tmp[length(tmp)]
+ }
+ ArrowFiles <- createArrowFiles(
+ inputFiles = file_list,
+ sampleNames = file_name,
+ minTSS = 0, #Dont set this too high because you can always increase later
+ minFrags = 0,
+ addTileMat = TRUE,
+ addGeneScoreMat = TRUE
+ )
+ proj = ArchRProject(
+ ArrowFiles = ArrowFiles,
+ outputDirectory = output.name,
+ copyArrows = F #This is recommened so that you maintain an unaltered copy for later usage.
+ )
+ #proj <- filterDoublets(ArchRProj = proj)
+ if (!is.null(gr)){
+ proj = addFeatureMatrix(proj,features = gr,matrixName='FeatureMatrix')
+ proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "FeatureMatrix", name = "IterativeLSI")
+ }
+ else{
+ proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "TileMatrix", name = "IterativeLSI")
+ }
+ proj <- addClusters(input = proj, reducedDims = "IterativeLSI",maxClusters=num_cluster)
+ proj <- addUMAP(ArchRProj = proj, reducedDims = "IterativeLSI")
+ p1 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Sample", embedding = "UMAP")
+ p2 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Clusters", embedding = "UMAP")
+ ggAlignPlots(p1, p2, type = "h")
+ rname = row.names(getCellColData(proj))
+ cluster = unlist(proj$Clusters%>%lapply(function(x){strsplit(x,'C')[[1]][2]%>%as.numeric}))
+ pre = cbind(rname,cluster)%>%as.data.frame
+ pre$cluster = as.numeric(pre$cluster)
+ #saveArchRProject(ArchRProj = proj, outputDirectory = paste0(output.name,'_save'), load = FALSE)
+ return(pre)
+}
+library(parallel)
+res = get_cluster("sort.HSC.fragments.tsv.gz",'HSC_all',num_cluster = 10,gr=cpeaks.gr)
+
+```
+
+## Run Python Script Manually
+
+### main.py Parameters
+
+| Parameter | Default | Description |
+| ------ | ------ | ------ |
+| fragment_path | - | The input file must be *.tsv.gz file, which will be transformed to cPeak reference. |
+| barcode_path | - | If barcode file is given, barcode in the file or all barcodes in the fragment will be used. |
+| reference | hg38 | cPeak version, hg38 or hg19. |
+| type_saved | .mtx | The type of output file, .mtx or .h5ad. |
+| output | map2cpeaks_result | Output folder name. |
+| output_name | cell-cpeaks | Name of output files. |
+| num_cores | 10 | Number of cores to use. |
+
+
+### Version
+Python (>=3.7)
+
+### Requirments
+
+```
+numpy
+gzip
+tqdm
+```
+
+### Method 1: Map the sequencing reads (fragments.tsv.gz) in each sample/cell to generate cell-by-cPeak matrix (.mtx/.h5ad)
+
+Map the sequencing reads (fragments.tsv.gz) in each sample/cell to generate cell-by-cPeak matrix (.mtx/.h5ad)
+```bash
+usage:
+
+
+The output file contains a barcode.txt and an mtx file that stores the matrix of map results.
+
+### Method 2. Directly map the pre-identified features like peaks to cPeaks (NOT recommand)
+
+**This is not a good idea.** It may lose information in the genomic regions which are not included in pre-identfied features. Also, for bulk ATAC-seq data, the quantification of each cPeak is inaccurate.
+
+```bash
+usage: python main.py [--bed_path feature.bed]
+
+--bed_path, -bed: the input feature.bed file, for example, MACS2calledPeaks.bed.
+```
+
+
diff --git a/Tutorials/_sidebar.md b/Tutorials/_sidebar.md
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+* Test
\ No newline at end of file
diff --git a/Tutorials/index.html b/Tutorials/index.html
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+
+
+
+
+