GWAS_summary_input: GWAS_summary_input

In dengchunyu/scPagwas: A pathway-based polygenic integration method for inferring disease-associated individual cells in single cell sequencing data

GWAS_summary_input

Description

Input and progress the GWAS summary statistics data filter the maf and the sex chrom data

Usage

GWAS_summary_input(
  Pagwas = NULL,
  gwas_data = NULL,
  maf_filter = 0.01,
  Sex_filter = TRUE,
  MHC_filter = TRUE,
  gwas_z_filter = -1
)

Arguments

Pagwas	Pagwas format, deault is NULL.
gwas_data	A data set object, need a data frame format, There must be have colomue with 'chrom', 'pos', 'rsid', 'beta', 'se', 'maf'
maf_filter	Filter the maf, default is <0.01
Sex_filter	Filter the SNPs in Sex chromosome, default is TRUE.
MHC_filter	Filter the SNPs in MHC chromosome, default is TRUE.
gwas_z_filter	Filter the z-score, calculated by abs(beta/se)

Value

Returns a list:

gwas_data

filtered gwas summary data frame.

Author(s)

Chunyu Deng

Examples

library(scPagwas)
Pagwas <- list()
gwas_data <- bigreadr::fread2(system.file("extdata",
  "GWAS_summ_example.txt",
  package = "scPagwas"
))
Pagwas <- GWAS_summary_input(Pagwas = Pagwas, gwas_data = gwas_data)
head(Pagwas$gwas_data)
# chrom     pos       rsid        se         beta     maf
# 1  chr1 1138913  rs3819001 0.0724484 4.774317e-01 0.06761
# 2  chr1 1691050 rs35672141 0.0368296 1.108626e-01 0.48200
# 3  chr1 1722932  rs3737628 0.0324817 7.881858e-02 0.48820
# 4  chr1 2369498  rs4592207 0.0399954 8.822031e-02 0.41790
# 5  chr1 3259369  rs2483286 0.0352555 1.541813e-03 0.30470
# 6  chr1 3811790 rs12085743 0.0405247 2.465163e-05 0.18500

dengchunyu/scPagwas documentation built on Nov. 29, 2024, 2:53 p.m.