Currently supports ortholog mapping between any pair of 700+ species.
Use map_species to return a full list of available organisms.

convert_orthologs(
  gene_df,
  gene_input = "rownames",
  gene_output = "rownames",
  standardise_genes = FALSE,
  input_species,
  output_species = "human",
  method = c("gprofiler", "homologene", "babelgene"),
  drop_nonorths = TRUE,
  non121_strategy = "drop_both_species",
  agg_fun = NULL,
  mthreshold = Inf,
  as_sparse = FALSE,
  as_DelayedArray = FALSE,
  sort_rows = FALSE,
  verbose = TRUE,
  ...
)

Arguments

gene_df

Data object containing the genes (see gene_input for options on how the genes can be stored within the object).
Can be one of the following formats:

  • matrix :
    A sparse or dense matrix.

  • data.frame :
    A data.frame, data.table. or tibble.

  • codelist :
    A list or character vector.

Genes, transcripts, proteins, SNPs, or genomic ranges can be provided in any format (HGNC, Ensembl, RefSeq, UniProt, etc.) and will be automatically converted to gene symbols unless specified otherwise with the ... arguments.
Note: If you set method="homologene", you must either supply genes in gene symbol format (e.g. "Sox2") OR set standardise_genes=TRUE.

gene_input

Which aspect of gene_df to get gene names from:

  • "rownames" :
    From row names of data.frame/matrix.

  • "colnames" :
    From column names of data.frame/matrix.

  • <column name> :
    From a column in gene_df, e.g. "gene_names".

gene_output

How to return genes. Options include:

  • "rownames" :
    As row names of gene_df.

  • "colnames" :
    As column names of gene_df.

  • "columns" :
    As new columns "input_gene", "ortholog_gene" (and "input_gene_standard" if standardise_genes=TRUE) in gene_df.

  • "dict" :
    As a dictionary (named list) where the names are input_gene and the values are ortholog_gene.

  • "dict_rev" :
    As a reversed dictionary (named list) where the names are ortholog_gene and the values are input_gene.

standardise_genes

If TRUE AND gene_output="columns", a new column "input_gene_standard" will be added to gene_df containing standardised HGNC symbols identified by gorth.

input_species

Name of the input species (e.g., "mouse","fly"). Use map_species to return a full list of available species.

output_species

Name of the output species (e.g. "human","chicken"). Use map_species to return a full list of available 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.

drop_nonorths

Drop genes that don't have an ortholog in the output_species.

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.

agg_fun

Aggregation function passed to aggregate_mapped_genes. Set to NULL to skip aggregation step (default).

mthreshold

Maximum number of ortholog names per gene to show. Passed to gorth. Only used when method="gprofiler" (DEFAULT : Inf).

as_sparse

Convert gene_df to a sparse matrix. Only works if gene_df is one of the following classes:

  • matrix

  • Matrix

  • data.frame

  • data.table

  • tibble

If gene_df is a sparse matrix to begin with, it will be returned as a sparse matrix (so long as gene_output= "rownames" or "colnames").

as_DelayedArray

Convert aggregated matrix to DelayedArray.

sort_rows

Sort gene_df rows alphanumerically.

verbose

Print messages.

...

Additional arguments to be passed to gorth or homologene.

NOTE: To return only the most "popular" interspecies ortholog mappings, supply mthreshold=1 here AND set method="gprofiler" above. 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.

For more details, please see here.

Value

gene_df with orthologs converted to the output_species.

Instead returned as a dictionary (named list) if gene_output="dict" or "dict_rev".

Examples

data("exp_mouse")
gene_df <- convert_orthologs(
    gene_df = exp_mouse,
    input_species = "mouse"
)
#> Preparing gene_df.
#> sparseMatrix format detected.
#> Extracting genes from rownames.
#> 15,259 genes extracted.
#> Converting mouse ==> human orthologs using: gprofiler
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: mouse
#> Common name mapping found for mouse
#> 1 organism identified from search: mmusculus
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: hsapiens
#> Checking for genes without orthologs in human.
#> Extracting genes from input_gene.
#> 16,022 genes extracted.
#> Extracting genes from ortholog_gene.
#> 16,022 genes extracted.
#> Dropping 2,659 NAs of all kinds from ortholog_gene.
#> Checking for genes without 1:1 orthologs.
#> Dropping 452 genes that have multiple input_gene per ortholog_gene (many:1).
#> Dropping 341 genes that have multiple ortholog_gene per input_gene (1:many).
#> Filtering gene_df with gene_map
#> Setting ortholog_gene to rownames.
#> Loading required namespace: DelayedArray
#> 
#> =========== REPORT SUMMARY ===========
#> Total genes dropped after convert_orthologs :
#>    2,908 / 15,259 (19%)
#> Total genes remaining after convert_orthologs :
#>    12,351 / 15,259 (81%)