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SCARP (Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information)

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Authors:

[email protected]

[email protected]

Usage:

Step1: Data Preprocessing

data = sc.read_h5ad(data_file_name)
data = sort_peaks(data)

Step2: Run SCARP

(1) parameter descriptions:

parameter name	description	type	default
data	input scATAC-seq data	AnnData object	None
m	diffusion intensity of NR	float	1.5
merge_thre	threshold to merge adjacent chromosomes	int	3000
beta	control the extent to which prior edge weight decays	float	1.5
return_shape	shape of return matrix	str	'CN'
peak_loc	adding prior weight or not	int	True
parallel	parallel computing or not	int	0

(2) return:

parameter name	description	type
t	running time	float
diffusion_mat	NR diffused matrix	matrix

(3) running example:

t, diffusion_mat = SCARP(data = adata,
                         m = 1.5,
                         merge_thre = 3000,
                         beta = 5000,
                         return_shape = 'CN',
                         peak_loc = True,
                         parallel = 0)

Step3: Compute Embedding Dimension

running example:

k = std_plot(data = diffusion_mat,
             title = 'Kept component',
             max_k = 50,
             plot_std = True,
             save_file = 'Kept component.svg')

Directory structure：

├─Exp1_Benchmark                         
│  │  README.md
│  │  S01_Data_Preprocessing.ipynb
│  │  S02_Run_SCARP.ipynb  
│  ├─figures    
│  ├─Processed data
│  ├─Raw data         
│  └─Results
│          
├─Exp2_Robustness
│  │  S01_Filter_peaks.ipynb
│  │  S02_Run_SCARP.ipynb
│  ├─Processed data
│  └─Results
│          
├─Exp3_SNARE_seq
│  │  README.md
│  │  S01_Signac_vignette.R
│  │  S02_Run_SCARP.ipynb
│  │  S03_Cells_clustering.R
│  │  SNARE.Rproj
│  ├─Processed data
│  ├─Raw data        
│  └─Results
│          
├─Exp4_SOX10_Knockdown
│  │  Exp4_SOX10_Knockdown.Rproj
│  │  README.md
│  │  S01_Run_SCARP.ipynb
│  │  S02_Peak annotation.R
│  │  S03_Computing_coaccessible_peaks.ipynb
│  │  S04_Gene_Enrichment.R
│  │  S05_Survival_analysis.R
│  ├─figures    
│  ├─Processed data    
│  ├─Results        
│  ├─Survival result   
│  └─TCGA
│          
├─Exp5_10X_Multiome
│  │  help_func.py
│  │  README.md
│  │  S01_Data_Preprocessing.ipynb
│  │  S02_Run_SCARP_CD4naive.ipynb
│  │  S03_PCHIC_Enhancer_CD4naive.ipynb
│  │  S04_Chip_Seq.ipynb
│  │  S05_Differential_Analysis.ipynb 
│  ├─figures   
│  ├─Processed data  
│  ├─Raw data            
│  └─Results
│          
└─Scarp
    │  data_preprocessing.py
    │  downstream.py
    │  SCARP_help_func.py
    └─

Reproduce results

1. Follow instructions in each subfile to prepare the necessary data.
2. Run the code step by step.