Calculate celltype associations using MAGMA — calculate_celltype

Assumes that you have already run map_snps_to_genes.

calculate_celltype_associations(
  ctd,
  ctd_levels = seq_len(length(ctd)),
  ctd_species = infer_ctd_species(ctd),
  gwas_sumstats_path = NULL,
  magma_dir = NULL,
  analysis_name = "MainRun",
  prepare_ctd = TRUE,
  upstream_kb = 35,
  downstream_kb = 10,
  genesOutCOND = NA,
  EnrichmentMode = "Linear",
  force_new = FALSE,
  version = NULL,
  verbose = TRUE
)

Arguments

ctd: Cell type data structure containing specificity_quantiles.
ctd_levels: Which levels of ctd to iterate the enrichment analysis over.
ctd_species: Species name relevant to the CellTypeDataset (ctd). See list_species for all available species. If ctd_species=NULL (default), the ctd species will automatically be inferred using infer_species.
gwas_sumstats_path: File path of the summary statistics file.
magma_dir: Path to folder containing the pre-computed MAGMA GWAS files (.gsa.rawand .gsa.out).
analysis_name: Used in file names which area created.
prepare_ctd: Whether to run prepare_quantile_groups on the ctd first.
upstream_kb: How many kb upstream of the gene should SNPs be included?
downstream_kb: How many kb downstream of the gene should SNPs be included?
genesOutCOND: [Optional] Path to a genes.out file to condition on. Used if you want to condition on a different GWAS.
EnrichmentMode: [Optional] Should either 'Linear' or 'Top 10%' mode be used for testing enrichment?
force_new: [Optional] Force new MAGMA analyses even if the pre-existing results files are detected.
version: MAGMA version to use.
verbose: Print messages.

Value

File path for the genes.out file

Examples

#### Prepare cell-type data ####
ctd <- ewceData::ctd()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache

#### Prepare GWAS MAGMA data ####
magma_dir <- MAGMA.Celltyping::import_magma_files(ids = "ieu-a-298")
#> Using built-in example files: ieu-a-298.tsv.gz.35UP.10DOWN
#> Returning MAGMA directories.
    
#### Run pipeline ####
ctAssocs <- calculate_celltype_associations(
    ctd = ctd,
    ctd_levels = 1,
    magma_dir = magma_dir,
    ctd_species = "mouse")
#> Installed MAGMA version: v1.10
#> Skipping MAGMA installation.
#> The desired_version of MAGMA is currently installed: v1.10
#> Using: magma_v1.10
#> Standardising CellTypeDataset
#> Found 5 matrix types across 2 CTD levels.
#> Processing level: 1
#> Processing level: 2
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Running MAGMA: Linear mode
#> Mapping gene symbols in specificity_quantiles matrix to entrez IDs.
#> Reading enrichment results file into R.