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PACS

Illustration of PACS framework.

PACS (Probabilistic model for Accessible Chromatin data in Single Cell)

PACS is a toolkit for snATAC-seq data analysis.

The key functionalities of PACS include:

  1. Depth correction for snATAC-seq data.
  2. Statistical test for complex data design
  3. Supervised cell type annotation (with a reference dataset)
  4. Batch effect correction enabled by statistical test framework

Installation

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).

Example

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
)

Vignette and Interactive Notebooks

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 .

Citation:

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

snATAC-seq toolkits

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

Contact

For any questions regarding the package, please contact Zhen Miao () or Junhyong Kim ().