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Prepare quantile groups for each celltype based on specificity

Source: R/prepare_quantile_groups.r
prepare_quantile_groups.Rd

Quantile groups are stored in an extra matrix ('quantiles') in the returned CTD. This function also removes any genes from the CTD data which are not 1:1 orthologs with the GWAS species.

Usage

prepare_quantile_groups(
  ctd,
  standardise = TRUE,
  non121_strategy = "drop_both_species",
  input_species = "mouse",
  output_species = "human",
  numberOfBins = 40,
  verbose = TRUE,
  ...
)

Arguments

ctd

Input CellTypeData.

standardise

Whether to run standardise_ctd first. Provides gene ortholog conversion.

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.

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.

numberOfBins

Number of non-zero quantile bins.

verbose

Print messages. Set verbose=2 if you want to print all messages from internal functions as well.

...

Additional arguments passed to standardise_ctd.

Value

The ctd converted to output_species gene symbols with additional quantiles matrix.

Examples

ctd_orig <- ewceData::ctd()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
ctd <- MAGMA.Celltyping::prepare_quantile_groups(ctd = ctd_orig)
#> Standardising CellTypeDataset
#> Found 5 matrix types across 2 CTD levels.
#> Processing level: 1
#> Processing level: 2
#> Converting to sparse matrix.
#> Converting to sparse matrix.