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
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")
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,# gene position in chromosome is provided by package. default is hg38, block_annotation_37 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.
)
)