PACS is a toolkit for snATAC-seq data analysis.
The key functionalities of PACS include:
- Depth correction for snATAC-seq data.
- Statistical test for complex data design
- Supervised cell type annotation (with a reference dataset)
- Batch effect correction enabled by statistical test framework
You can install the current version of PACS from GitHub with:
# install.packages("devtools")
devtools::install_github("Zhen-Miao/PICsnATAC") ## dependent package
devtools::install_github("Zhen-Miao/PACS")This installation step should take < 5 min on a standard desktop
machine. R version > 4.0.0 is recommended (older R version has not
been tested).
This is a basic example which shows you how to run PACS common workflow:
library(PACS)
p_vals <- pacs_test_sparse(
covariate_meta.data = meta.data,
formula_full = ~ factor(cell_type) + factor(batch),
formula_null = ~ factor(batch),
pic_matrix = data_mat,
cap_rates = capturing_probability
)An interactive tutorial website with vignettes can be found at
https://zhen-miao.github.io/PACS/
Although not recommended, you can also run the analysis using Goolge Colab, with one example shown below:
Notebook 1: Type 1 error and power evaluation in real data .
Miao, Z., Wang, J., Park, K. Kuang, D. and Kim, J. Model-based compound hypothesis testing for snATAC-seq data with PACS. bioRxiv (2023). https://www.biorxiv.org/content/10.1101/2023.07.30.551108v1
PACS is one of the snATAC-seq toolkits developed by the Kim lab. Other toolkits include:
Miao, Z and Kim, J. Uniform quantification of single-nucleus ATAC-seq data with Paired- Insertion Counting (PIC) and a model-based insertion rate estimator. Nature Methods 2024
For any questions regarding the package, please contact Zhen Miao (zhenmiao@pennmedicine.upenn.edu) or Junhyong Kim (junhyong@sas.upenn.edu).