GSA.Tissue.Celltype.Enrich

Ana Cristina Gonzalez Sanchez
University of Bologna / Karolinska Institutet
Contact

• Introduction
• Installation
• Geneset Enrichment Analysis
  • GWAS formatting
  • Running GSA
• Tissue Enrichment Analysis
  • Dataset preparation
  • Genloc & GWAS preparation

Introduction

GSA_Tissue_Celltype_Enrich is an R library intented to carry out in an easy manner Tissue enrichment analysis using data from GTEX, Celltype Enrichment Analysis using MAGMA-Celltyping or Geneset Enrichment Analysis for list of genes coming from differential expression analysis. Over representation of list of genes is tested in GWAS summary statistics.

Installation

if(!"devtools" %in% row.names(installed.packages())){
  install.packages("devtools")
}
library(devtools)

if(!"GSA_Tissue_Celltype_Enrich" %in% row.names(installed.packages())){
  install_github("Anacristina0914/GSA.Tissue.Celltype.Enrich")
}
library(GSA_Tissue_Celltype_Enrich) 

Gene Set Enrichment Analysis (GSA)

GWAS Summary Statistics Formatting

GWAS summary statistics must be formatted as specified in the MAGMA guidance. Briefly, the first three columns have to correspond to the SNP, CHR and BP (see example below):

SNP	CHR	BP	P
rs1345	2	100123	0.01
rs18667	3	30566921	0.5611
rs145	16	9992021	0.173

This can be achieved either manually or using the formula format_sumstats_for_magma.r from the MAGMA_Celltyping package.

GSA Enrichment for up and down regulated genes in GTEx data

# Load required libraries for the package
load_required_libraries()

# Load GTEx gene expression data from Soma and Axon in human Motor Neurons. Only controls are loaded (C*).  
Human_SomaAxon_data <- GSA.Tissue.Celltype.Enrich::load_GTEx_data_conditional(path = "/Soma_Axon_RNA-Seq/GSE121069_GEO_rpkms_human.txt",pattern = "C*",sep = "\t")

# Load GTEx data annotation
Human_SomaAxon_annot <- GSA.Tissue.Celltype.Enrich::load_GTEx_annot_conditional(path = "/Soma_Axon_RNA-seq/", data = Human_SomaAxon, data_type = "Soma-Axon")

# Run Differential Expression analysis 
tt_human_SomaAxon <- GSA.Tissue.Celltype.Enrich::run_diffExp_analysis(annot = Human_SomaAxon_annot, data = Human_SomaAxon_data, expr_path = "/Soma_Axon_RNA-seq/", analysis_type = "D_Soma-Axon", species = "human")

# Map snps to genes and generate gene level p-value from summary statistics
genes.raw_path <- GSA.Tissue.Celltype.Enrich::map_snps_to_genes(gwas_path = "/ALS_sumstats.txt", N=NULL, genloc_filepath = "/genloc_files/NCBI37.3.gene.loc", genome_ref_path = "/g1000/g1000_eur",analysis_type = "D_Soma-Axon", species = "human")

# Run Gene Set Analysis (GSA) using MAGMA and up/down regulated genes from expression data and GWAS summary statistics.
GSA.Tissue.Celltype.Enrich::MAGMA_GSA(tt_filename = tt_human_SomaAxon, analysis_type = "D_Soma-Axon", genes.raw_path = genes.raw_path, species = "human", gene_n = 250)

Tissue Enrichment Analysis

Tissue Dataset Preparation

GTEx analysis v8 2017-06-05 gene-level median TPM by tissue dataset was retrieved from the GTEx database and further processed as described in Bryois et al. 2020[1] and its corresponding github repository jbryois/scRNA_disease. The final file used for the tissue enrichment analysis (top10.txt) contains the 10% most specific genes by tissue, where specificity is calculated dividiving the expression of each gene in a tissue divided by total expression of that gene in all tissues.

GWAS Summary Statistics and Genloc file Prepation

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

References

[1] Bryois, J., Skene, N. G., Hansen, T. F., Kogelman, L. J., Watson, H. J., Liu, Z., ... & Sullivan, P. F. (2020). Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nature genetics, 52(5), 482-493.

License

MIT