Get genes driving significant MAGMA_celltyping results — get_driver

Infers the genes driving significant cell-type-specific enrichment results by computing the mean rank of the adjusted Z-score from the GWAS gene annotation file ("ADJ_ZSTAT") and the cell-type specificity score from the CellTypeDataset ("specificity_proportion").

Usage

get_driver_genes(
  ctd,
  ctd_species = infer_ctd_species(ctd = ctd),
  prepare_ctd = TRUE,
  magma_res,
  GenesOut_dir,
  fdr_thresh = 0.05,
  n_genes = 100,
  spec_deciles = NULL,
  verbose = TRUE,
  ...
)

Arguments

ctd

CellTypeData object

ctd_species

Either 'human' or 'mouse'

prepare_ctd

Whether to run prepare_quantile_groups on the ctd first.

magma_res

Merged results from merge_results.

GenesOut_dir

Folder to search for .genes.out files implicated in magma_res.

fdr_thresh

FDR threshold for magma_res.

n_genes

Max number of drive genes to return per cell-type enrichment.

spec_deciles

[Optional] Which "specificity_proportion" deciles to include when calculating driver genes. (10 = most specific).

verbose

Print messages.

...

Arguments passed on to EWCE::standardise_ctd

dataset

CellTypeData. name.

input_species

Which species the gene names in exp come from. See list_species for all available species.

output_species

Which species' genes names to convert exp to. See list_species for all available species.

sctSpecies_origin

Species that the sct_data originally came from, regardless of its current gene format (e.g. it was previously converted from mouse to human gene orthologs). This is used for computing an appropriate backgrund.

non121_strategy

How to handle genes that don't have 1:1 mappings between input_species:output_species. Options include:

"drop_both_species" or "dbs" or 1: Drop genes that have duplicate mappings in either the input_species or output_species (DEFAULT).
"drop_input_species" or "dis" or 2: Only drop genes that have duplicate mappings in the input_species.
"drop_output_species" or "dos" or 3: Only drop genes that have duplicate mappings in the output_species.
"keep_both_species" or "kbs" or 4: Keep all genes regardless of whether they have duplicate mappings in either species.
"keep_popular" or "kp" or 5: Return only the most "popular" interspecies ortholog mappings. This procedure tends to yield a greater number of returned genes but at the cost of many of them not being true biological 1:1 orthologs.
"sum","mean","median","min" or "max": When gene_df is a matrix and gene_output="rownames", these options will aggregate many-to-one gene mappings (input_species-to-output_species) after dropping any duplicate genes in the output_species.

method

R package to use for gene mapping:

"gprofiler": Slower but more species and genes.
"homologene": Faster but fewer species and genes.
"babelgene": Faster but fewer species and genes. Also gives consensus scores for each gene mapping based on a several different data sources.

force_new_quantiles

By default, quantile computation is skipped if they have already been computed. Set =TRUE to override this and generate new quantiles.

force_standardise

If ctd has already been standardised, whether to rerun standardisation anyway (Default: FALSE).

remove_unlabeled_clusters

Remove any samples that have numeric column names.

numberOfBins

Number of non-zero quantile bins.

keep_annot

Keep the column annotation data if provided.

keep_plots

Keep the dendrograms if provided.

as_sparse

Convert to sparse matrix.

as_DelayedArray

Convert to DelayedArray.

rename_columns

Remove replace_chars from column names.

make_columns_unique

Rename each columns with the prefix dataset.species.celltype.

Examples

ctd <- ewceData::ctd()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
GenesOut_dir <- MAGMA.Celltyping::import_magma_files()
#> Using built-in example files: ieu-a-298.tsv.gz.35UP.10DOWN
#> Returning MAGMA directories.
magma_res <- MAGMA.Celltyping::merge_results(
    MAGMA.Celltyping::enrichment_results)
#> Saving full merged results to ==> /tmp/RtmpEkCLL9/MAGMA_celltyping./.lvl1.csv
genesets <- MAGMA.Celltyping::get_driver_genes(ctd = ctd, 
                                               magma_res = magma_res, 
                                               GenesOut_dir = GenesOut_dir, 
                                               fdr_thresh = 1)
#> Filtering @ FDR< 1
#> 1 genes.out file(s) found.
#> ctd_species=NULL: Inferring species from gene names.
#> Preparing gene_df.
#> Dense matrix format detected.
#> Extracting genes from rownames.
#> 15,259 genes extracted.
#> Testing for gene overlap with: human
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-9606-homologene.csv.gz
#> Returning all 19,129 genes from human.
#> Testing for gene overlap with: monkey
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: monkey
#> Common name mapping found for monkey
#> 1 organism identified from search: 9544
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-9544-homologene.csv.gz
#> Returning all 16,843 genes from monkey.
#> Testing for gene overlap with: rat
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: rat
#> Common name mapping found for rat
#> 1 organism identified from search: 10116
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-10116-homologene.csv.gz
#> Returning all 20,616 genes from rat.
#> Testing for gene overlap with: mouse
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: mouse
#> Common name mapping found for mouse
#> 1 organism identified from search: 10090
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-10090-homologene.csv.gz
#> Returning all 21,207 genes from mouse.
#> Testing for gene overlap with: zebrafish
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: zebrafish
#> Common name mapping found for zebrafish
#> 1 organism identified from search: 7955
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-7955-homologene.csv.gz
#> Returning all 20,897 genes from zebrafish.
#> Testing for gene overlap with: fly
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: fly
#> Common name mapping found for fly
#> 1 organism identified from search: 7227
#> Using cached file: /github/home/.cache/R/orthogene/all_genes-7227-homologene.csv.gz
#> Returning all 8,438 genes from fly.
#> Top match:
#>   - species: mouse 
#>   - percent_match: 92%
#> Inferred ctd species: mouse
#> Standardising CellTypeDataset
#> Found 5 matrix types across 2 CTD levels.
#> Processing level: 1
#> Processing level: 2
#> + Finding driver genes for: ieu-a-298 GWAS x CTD
#> Importing genes.out file.
#> 4 genes without HGNC gene symbols were dropped.
#> 371 genes that are absent from the ctd were dropped.
#> Computing adjusted Z-statistic.
#> + Level 1
#> Preparing specificity matrix.
#> 7 cell-types selected.
#> Running enrichment tests: astrocytes_ependymal
#> Removing intercept from test coefficients
#> Running enrichment tests: endothelial_mural
#> Removing intercept from test coefficients
#> Running enrichment tests: interneurons
#> Removing intercept from test coefficients
#> Running enrichment tests: microglia
#> Removing intercept from test coefficients
#> Running enrichment tests: oligodendrocytes
#> Removing intercept from test coefficients
#> Running enrichment tests: pyramidal_CA1
#> Removing intercept from test coefficients
#> Running enrichment tests: pyramidal_SS
#> Removing intercept from test coefficients
#> Identifying top driver genes per cell-type association:
#>    astrocytes_ependymal : 100 driver genes
#>    endothelial_mural : 100 driver genes
#>    interneurons : 100 driver genes
#>    microglia : 100 driver genes
#>    oligodendrocytes : 100 driver genes
#>    pyramidal_CA1 : 100 driver genes
#>    pyramidal_SS : 100 driver genes