About scPagwas

scPagwas employs a polygenic regression model to prioritize a set of trait-relevant genes and uncover trait-relevant cell subpopulations by incorporating pathway activity transformed single-cell RNA sequencing (scRNA-seq) data with genome-wide association studies (GWAS) summary data.

Please cite this article in press as: Ma et al.,Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data,Cell Genomics (2023),https://doi.org/10.1016/j.xgen.2023.100383

Code for reproducing the analysis from the paper is available here, or

For further usage on the scPagwas package, you can visit the website. A vignette for using also can be accessed using browseVignettes(“scPagwas”)

Some important data can be download from here

Installation

You can install the released version of scPagwas from github with:

#install some dependence packages
install.packages("Seurat")
install.packages("ggpubr")
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("GenomicRanges")
BiocManager::install("IRanges")

devtools::install_github("sulab-wmu/scPagwas")

In many cases, installing packages using devtools::install_github may fail.

library(devtools)
install_git("https://github.com/sulab-wmu/scPagwas.git", ref = "main")

Or, download the package file from here Then install it locally.

devtools::install_local("sulab-wmu-scPagwas-****.tar.gz")

Usage

quick-start example:

library(scPagwas)
system.time(
 #1.start to run the wrapper functions for example.
 Pagwas_data<-scPagwas_main(Pagwas = NULL,
                     gwas_data =system.file("extdata", "GWAS_summ_example.txt", package = "scPagwas"), # The GWAS Summary statistics files 
                     Single_data =system.file("extdata", "scRNAexample.rds", package = "scPagwas"),# scRNA-seq data in seruat format with "RNA" assays and normalized.
                     output.prefix="test", # the prefix name for output files
                     output.dirs="scPagwastest_output",# the directory file's name for output
                     block_annotation = block_annotation_hg37,# gene position in chromosome is provided by package. default is hg38, block_annotation_hg37 is hg37.
                     assay="RNA", # the assays for scRNA-seq data to use.
                     Pathway_list=Genes_by_pathway_kegg,# pathway list is provided by package, including gene symbols.
                     n.cores=1,
                     iters_singlecell = 10,
                     chrom_ld = chrom_ld,# The LD data is provided by package.
                     singlecell=T, # Whether to run the singlecell process.
                     celltype=T# Whether to run the celltype process.
)
)

All COVID-19-related projects in our group:

1. Meta-analysis of large-scale GWAS data to uncover novel loci for COVID-19. see Ma et al. Human Molecular Genetics, 2021, and see related Github codes.
2. COVID-19 Quarantine Reveals That Behavioral Changes Have an Effect on Myopia Progression. see Xu, Ma et al. Ophthalmology, 2021, see related Github codes.
3. Identification of genetics-influenced immune cell sub-populations relevant to severe COVID-19. see Ma et al. Genome Medicine, 2022, and see related Github codes.
4. Repurposing cell type-specific durg targets for severe COVID-19 based on human organoids scRNA-seq atlas. see Ma et al. Cell Proliferation, 2023, and see related Github codes
5. Development of novel polygenic regression method scPagwas for integrating scRNA-seq data with GWAS on complex diseases. see Ma et al. Cell Genomics, 2023, and see related Github codes