Perform calculations for multiple traits based on a single-cell dataset

Last Updated: 24, April, 2024 at 09:47

This vignette description a situation that multiple traits files are running for one single cell data. In this case, we can running single cell function for prepare and save the result for the following traits’ code program. The example trait files are:

• monocytecount
• Lymphocytecount
• MeanCorpusVolume The processed gwas data can be download from here.

BMMC example scRNA-seq data can be obtained from here.

Prepare the Single cell data firstly

The single cell Intermediate data is immobile for the same Single_data input no matter the gwas_data input files. First, Run the Single_data_input and Pathway_pcascore_run for Seu_Hema_data.rds.

library(scPagwas)
Pagwas <- list()
Single_data<-readRDS('Seu_Hema_data.rds')
Pagwas <- Single_data_input(Pagwas=Pagwas,
                            assay="RNA",
                            Single_data=Single_data,
                            Pathway_list=Genes_by_pathway_kegg)
Pagwas <- Pathway_pcascore_run(Pagwas=Pagwas,
                               Pathway_list=Genes_by_pathway_kegg)

Perform scPagwas calculations separately for each trait

We take the Pagwas as input for scPagwas_main, therefore, there is no need to run the Pathway_pcascore_run function for different traits which will save times.

Use the previous single-cell result data as input for scPagwas_main. By doing so, the intermediate results from the single-cell analysis will be carried over to the new computation, saving time and computational resources.

#Pagwas is the result for last blocks. It inherits the single cell result in the previous result
Pagwas_monocytecount<-scPagwas_main(Pagwas =Pagwas,
                     gwas_data ='monocytecount_gwas_data.txt',
                     Single_data ='Seu_Hema_data.rds',
                     output.prefix="monocytecount_scPagwas",
                     output.dirs="monocytecount_bmmc",
                     Pathway_list=Genes_by_pathway_kegg,
                     assay="RNA",
                     block_annotation = block_annotation,
                     iters_singlecell = 100,
                     chrom_ld = chrom_ld,# The LD data is provided by package.
                     singlecell=T, # Whether to run the singlecell process.
                     celltype=T)

Pagwas_Lymphocytecount<-scPagwas_main(Pagwas =Pagwas,
                     gwas_data ="Lymphocytecount_gwas_data.txt",
                     Single_data ='Seu_Hema_data.rds',
                     output.prefix="Lymphocytecountt_scPagwas",
                     output.dirs="Lymphocytecount_bmmc",
                     Pathway_list=Genes_by_pathway_kegg,
                     assay="RNA",
                     block_annotation = block_annotation,
                     iters_singlecell = 100,
                     chrom_ld = chrom_ld,# The LD data is provided by package.
                     singlecell=T, # Whether to run the singlecell process.
                     celltype=T)

Pagwas_MeanCorpusVolume<-scPagwas_main(Pagwas =Pagwas,
                     gwas_data ="MeanCorpusVolume_prune_gwas_data.txt",
                     Single_data ='Seu_Hema_data.rds',
                     output.prefix="MeanCorpusVolume_scPagwas",
                     output.dirs="MeanCorpusVolume_bmmc",
                     Pathway_list=Genes_by_pathway_kegg,
                     assay="RNA",
                     block_annotation = block_annotation,
                     iters_singlecell = 100,
                     chrom_ld = chrom_ld,# The LD data is provided by package.
                     singlecell=T, # Whether to run the singlecell process.
                     celltype=T)