Package 'orthogene' reference manual

Title:	Interspecies gene mapping
Description:	`orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across 700+ organisms. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using 1:1, many:1, 1:many or many:many gene mappings, both within- and between-species.
Authors:	Brian Schilder [cre]
Maintainer:	Brian Schilder <[email protected]>
License:	GPL-3
Version:	1.13.0
Built:	2024-10-30 09:22:47 UTC
Source:	https://github.com/bioc/orthogene

orthogene: Interspecies gene mapping

Description

orthogene is an R package for easy mapping of orthologous genes across hundreds of species.

Details

It pulls up-to-date interspecies gene ortholog mappings across 700+ organisms.
It also provides various utility functions to map common objects (e.g. data.frames, gene expression matrices, lists) onto 1:1 gene orthologs from any other species.

Author(s)

Maintainer: Brian Schilder [email protected] (ORCID)

Source

GitHub : Source code and Issues submission.
Author Site : orthogene was created by Brian M. Schilder.

Aggregate/expand a gene matrix by gene mappings

Description

Aggregate/expand a gene matrix (gene_df) using a gene mapping data.frame (gene_map). Importantly, mappings can be performed across a variety of scenarios that can occur during within-species and between-species gene mapping:

1 gene : 1 gene
many genes : 1 gene
1 gene : many genes
many genes : many genes

For more details on how aggregation/expansion is performed, please see: many2many_rows.

Usage

aggregate_mapped_genes(
  gene_df,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  input_species = "human",
  output_species = input_species,
  method = c("gprofiler", "homologene", "babelgene"),
  agg_fun = "sum",
  agg_method = c("monocle3", "stats"),
  aggregate_orthologs = TRUE,
  transpose = FALSE,
  mthreshold = 1,
  target = "ENSG",
  numeric_ns = "",
  as_integers = FALSE,
  as_sparse = TRUE,
  as_DelayedArray = FALSE,
  dropNA = TRUE,
  sort_rows = FALSE,
  verbose = TRUE
)
aggregate_mapped_genes(
  gene_df,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  input_species = "human",
  output_species = input_species,
  method = c("gprofiler", "homologene", "babelgene"),
  agg_fun = "sum",
  agg_method = c("monocle3", "stats"),
  aggregate_orthologs = TRUE,
  transpose = FALSE,
  mthreshold = 1,
  target = "ENSG",
  numeric_ns = "",
  as_integers = FALSE,
  as_sparse = TRUE,
  as_DelayedArray = FALSE,
  dropNA = TRUE,
  sort_rows = FALSE,
  verbose = TRUE
)

Arguments

`gene_df`	Input matrix where row names are genes.
`gene_map`	A data.frame that maps the current gene names to new gene names. This function's behaviour will adapt to different situations as follows: `gene_map=<data.frame>` : When a data.frame containing the gene key:value columns (specified by `input_col` and `output_col`, respectively) is provided, this will be used to perform aggregation/expansion. `gene_map=NULL` and `input_species!=output_species` : A `gene_map` is automatically generated by map_orthologs to perform inter-species gene aggregation/expansion. `gene_map=NULL` and `input_species==output_species` : A `gene_map` is automatically generated by map_genes to perform within-species gene gene symbol standardization and aggregation/expansion.
`input_col`	Column name within `gene_map` with gene names matching the row names of `X`.
`output_col`	Column name within `gene_map` with gene names that you wish you map the row names of `X` onto.
`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.
`agg_fun`	Aggregation function.
`agg_method`	Aggregation method.
`aggregate_orthologs`	[Optional] After performing an initial round of many:many aggregation/expansion with many2many_rows, ensure each orthologous gene only appears in one row by using the aggregate_rows function (default: `TRUE`).
`transpose`	Transpose `gene_df` before mapping genes.
`mthreshold`	maximum number of results per initial alias to show. Shows all by default.
`target`	target namespace.
`numeric_ns`	namespace to use for fully numeric IDs (list of available namespaces).
`as_integers`	Force all values in the matrix to become integers, by applying floor (default: `FALSE`).
`as_sparse`	Convert aggregated matrix to sparse matrix.
`as_DelayedArray`	Convert aggregated matrix to DelayedArray.
`dropNA`	Drop genes assigned to `NA` in `groupings`.
`sort_rows`	Sort `gene_df` rows alphanumerically.
`verbose`	Print messages.

Value

Aggregated matrix

Examples

#### Aggregate within species: gene synonyms ####
data("exp_mouse_enst")                                
X_agg <- aggregate_mapped_genes(gene_df = exp_mouse_enst, 
                                input_species = "mouse")  
                                 
#### Aggregate across species: gene orthologs ####               
data("exp_mouse")
X_agg2 <- aggregate_mapped_genes(gene_df = exp_mouse, 
                                 input_species = "mouse",
                                 output_species = "human",
                                 method="homologene")                                                     
#### Aggregate within species: gene synonyms ####
data("exp_mouse_enst")                                
X_agg <- aggregate_mapped_genes(gene_df = exp_mouse_enst, 
                                input_species = "mouse")  
                                 
#### Aggregate across species: gene orthologs ####               
data("exp_mouse")
X_agg2 <- aggregate_mapped_genes(gene_df = exp_mouse, 
                                 input_species = "mouse",
                                 output_species = "human",
                                 method="homologene")

Get all genes

Description

Return all known genes from a given species.

Usage

all_genes(
  species,
  method = c("gprofiler", "homologene", "babelgene"),
  ensure_filter_nas = FALSE,
  run_map_species = TRUE,
  verbose = TRUE,
  ...
)
all_genes(
  species,
  method = c("gprofiler", "homologene", "babelgene"),
  ensure_filter_nas = FALSE,
  run_map_species = TRUE,
  verbose = TRUE,
  ...
)

Arguments

`species`	Species to get all genes for. Will first be standardised with `map_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.
`ensure_filter_nas`	Perform an extra check to remove genes that are `NA`s of any kind.
`run_map_species`	Standardise `species` names with map_species first (Default: `TRUE`).
`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.

Details

References homologeneData or gconvert.

Value

Table with all gene symbols from the given species.

Examples

genome_mouse <- all_genes(species = "mouse")
genome_human <- all_genes(species = "human")
genome_mouse <- all_genes(species = "mouse")
genome_human <- all_genes(species = "human")

All species

Description

List all species currently supported by orthogene. Wrapper function for map_species. When method=NULL, all species from all available methods will be returned.

Usage

all_species(method = NULL, verbose = TRUE)
all_species(method = NULL, verbose = TRUE)

Arguments

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.

verbose

Print messages.

Value

data.table of species names, provided in multiple formats.

Examples

species_dt <- all_species()
species_dt <- all_species()

Map genes from one species to another

Description

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

Usage

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,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  verbose = TRUE,
  ...
)
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,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  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.
`gene_map`	A data.frame that maps the current gene names to new gene names. This function's behaviour will adapt to different situations as follows: `gene_map=<data.frame>` : When a data.frame containing the gene key:value columns (specified by `input_col` and `output_col`, respectively) is provided, this will be used to perform aggregation/expansion. `gene_map=NULL` and `input_species!=output_species` : A `gene_map` is automatically generated by map_orthologs to perform inter-species gene aggregation/expansion. `gene_map=NULL` and `input_species==output_species` : A `gene_map` is automatically generated by map_genes to perform within-species gene gene symbol standardization and aggregation/expansion.
`input_col`	Column name within `gene_map` with gene names matching the row names of `X`.
`output_col`	Column name within `gene_map` with gene names that you wish you map the row names of `X` onto.
`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"
)
data("exp_mouse")
gene_df <- convert_orthologs(
    gene_df = exp_mouse,
    input_species = "mouse"
)

Create gene background

Description

Create a gene background as the union/intersect of all orthologs between input species (species1 and species2), and the output_species. This can be useful when generating random lists of background genes to test against in analyses with data from multiple species (e.g. enrichment of mouse cell-type markers gene sets in human GWAS-derived gene sets).

Usage

create_background(
  species1,
  species2,
  output_species = "human",
  as_output_species = TRUE,
  use_intersect = TRUE,
  bg = NULL,
  gene_map = NULL,
  method = "homologene",
  non121_strategy = "drop_both_species",
  verbose = TRUE
)
create_background(
  species1,
  species2,
  output_species = "human",
  as_output_species = TRUE,
  use_intersect = TRUE,
  bg = NULL,
  gene_map = NULL,
  method = "homologene",
  non121_strategy = "drop_both_species",
  verbose = TRUE
)

Arguments

`species1`	First species.
`species2`	Second species.
`output_species`	Species to convert all genes from `species1` and `species2` to first. `Default="human"`, but can be to either any species supported by orthogene, including `species1` or `species2`.
`as_output_species`	Return background gene list as `output_species` orthologs, instead of the gene names of the original input species.
`use_intersect`	When `species1` and `species2` are both different from `output_species`, this argument will determine whether to use the intersect (`TRUE`) or union (`FALSE`) of all genes from `species1` and `species2`.
`bg`	User supplied background list that will be returned to the user after removing duplicate genes.
`gene_map`	User-supplied `gene_map` data table from map_orthologs or map_genes.
`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.
`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`.
`verbose`	Print messages.

Value

Background gene list.

Examples

bg <- orthogene::create_background(species1 = "mouse", 
                                   species2 = "rat",
                                   output_species = "human")
bg <- orthogene::create_background(species1 = "mouse", 
                                   species2 = "rat",
                                   output_species = "human")

Gene expression data: mouse

Description

Mean pseudobulk single-cell RNA-seq gene expression matrix.

Data originally comes from Zeisel et al., 2018 (Cell).

Usage

data("exp_mouse")
data("exp_mouse")

Format

sparse matrix

Source

Publication ctd <- ewceData::ctd() exp_mouse <- as(ctd[[1]]$mean_exp, "sparseMatrix") usethis::use_data(exp_mouse, overwrite = TRUE)

Transcript expression data: mouse

Description

Mean pseudobulk single-cell RNA-seq Transcript expression matrix.

Data originally comes from Zeisel et al., 2018 (Cell).

Usage

data("exp_mouse_enst")
data("exp_mouse_enst")

Format

sparse matrix

Source

Publication data("exp_mouse") mapped_genes <- map_genes(genes = rownames(exp_mouse)[seq(1,100)], target = "ENST", species = "mouse", drop_na = FALSE) exp_mouse_enst <- exp_mouse[mapped_genes$input,] rownames(exp_mouse_enst) <- mapped_genes$target all_nas <- orthogene:::find_all_nas(rownames(exp_mouse_enst)) exp_mouse_enst <- exp_mouse_enst[!all_nas,] exp_mouse_enst <- phenomix::add_noise(exp_mouse_enst) usethis::use_data(exp_mouse_enst, overwrite = TRUE)

Format species names

Description

Format scientific species names into a standardised manner.

Usage

format_species(
  species,
  remove_parentheses = TRUE,
  abbrev = FALSE,
  remove_subspecies = FALSE,
  remove_subspecies_exceptions = c("Canis lupus familiaris"),
  split_char = " ",
  collapse = " ",
  remove_chars = c(" ", ".", "(", ")", "[", "]"),
  replace_char = "",
  lowercase = FALSE,
  trim = "'",
  standardise_scientific = FALSE
)
format_species(
  species,
  remove_parentheses = TRUE,
  abbrev = FALSE,
  remove_subspecies = FALSE,
  remove_subspecies_exceptions = c("Canis lupus familiaris"),
  split_char = " ",
  collapse = " ",
  remove_chars = c(" ", ".", "(", ")", "[", "]"),
  replace_char = "",
  lowercase = FALSE,
  trim = "'",
  standardise_scientific = FALSE
)

Arguments

`species`	Species query (e.g. "human", "homo sapiens", "hsapiens", or 9606). If given a list, will iterate queries for each item. Set to `NULL` to return all species.
`remove_parentheses`	Remove substring within parentheses: e.g. "Xenopus (Silurana) tropicalis" –> "Xenopus tropicalis"
`abbrev`	Abbreviate all taxonomic levels except the last one: e.g. "Canis lupus familiaris" ==> "C l familiaris"
`remove_subspecies`	Only keep the first two taxonomic levels: e.g. "Canis lupus familiaris" –> "Canis lupus"
`remove_subspecies_exceptions`	Selected species to ignore when `remove_subspecies=TRUE`. e.g. "Canis lupus familiaris" –> "Canis lupus familiaris"
`split_char`	Character to split species names by.
`collapse`	Character to re-collapse species names with after splitting with `split_char`.
`remove_chars`	Characters to remove.
`replace_char`	Character to replace `remove_chars` with.
`lowercase`	Make species names all lowercase.
`trim`	Characters to trim from the beginning/end of each species name.
`standardise_scientific`	Automatically sets multiple arguments at once to create standardised scientific names for each species. Assumes that `species` is provided in some version of scientific species names: e.g. "Xenopus (Silurana) tropicalis" –> "Xenopus tropicalis"

Value

A named vector where the values are the standardised species names and the names are the original input species names.

Examples

species <- c("Xenopus (Silurana) tropicalis","Canis lupus familiaris")
species2 <- format_species(species = species, abbrev=TRUE)
species3 <- format_species(species = species, 
                           standardise_scientific=TRUE,
                           remove_subspecies_exceptions=NULL)
species <- c("Xenopus (Silurana) tropicalis","Canis lupus familiaris")
species2 <- format_species(species = species, abbrev=TRUE)
species3 <- format_species(species = species, 
                           standardise_scientific=TRUE,
                           remove_subspecies_exceptions=NULL)

Get silhouettes

Description

Get silhouette images of each species from phylopic.

Usage

get_silhouettes(
  species,
  which = rep(1, length(species)),
  run_format_species = TRUE,
  include_image_data = FALSE,
  mc.cores = 1,
  add_png = FALSE,
  remove_bg = FALSE,
  verbose = TRUE
)
get_silhouettes(
  species,
  which = rep(1, length(species)),
  run_format_species = TRUE,
  include_image_data = FALSE,
  mc.cores = 1,
  add_png = FALSE,
  remove_bg = FALSE,
  verbose = TRUE
)

Arguments

`species`	A character vector of species names to query phylopic for.
`which`	An integer vector of the same length as `species`. Lets you choose which image you want to use for each species (1st, 2nd 3rd, etc.).
`run_format_species`	Standardise species names with format_species before querying phylopic (default: `TRUE`).
`include_image_data`	Include the image data itself (not just the image UID) in the results.
`mc.cores`	Accelerate multiple species queries by parallelising across multiple cores.
`add_png`	Return URLs for both the SVG and PNG versions of the image.
`remove_bg`	Remove image background.
`verbose`	Print messages.

Value

data.frame with:

input_species : Species name (input).
species : Species name (standardised).
uid : Species UID.
url : Image URL.

Source

phylopic/rphylopic API changes

ggimage: Issue with finding valid PNGs

Examples

 
species <- c("Mus_musculus","Pan_troglodytes","Homo_sapiens")
uids <- get_silhouettes(species = species)
species <- c("Mus_musculus","Pan_troglodytes","Homo_sapiens")
uids <- get_silhouettes(species = species)

gconvert namespaces

Description

Available namespaces used by link[gprofiler2]gconvert.

Format

data.frame

Source

gProfiler site

#### Manually-prepared CSV #### path <- "inst/extdata/gprofiler_namespace.csv.gz" gprofiler_namespace <- data.table::fread(path)

Reference organisms

Description

Organism for which gene references are available via gProfiler API. Used as a backup if API is not available.

Format

data.frame

Source

gProfiler site

# NOTE!: Must run usethis::use_data for all internal data at once. # otherwise, the prior internal data will be overwritten. #### Internal data 1: gprofiler_namespace #### #### Manually-prepared CSV #### path <- "inst/extdata/gprofiler_namespace.csv.gz" gprofiler_namespace <- data.table::fread(path) #### Internal data 2: gprofiler_orgs gprofiler_orgs <- orthogene:::get_orgdb_gprofiler(use_local=FALSE) #### Save #### usethis::use_data(gprofiler_orgs,gprofiler_namespace, overwrite = TRUE, internal=TRUE)

Infer species from gene names

Description

Infers which species the genes within gene_df is from. Iteratively test the percentage of gene_df genes that match with the genes from each test_species.

Usage

infer_species(
  gene_df,
  gene_input = "rownames",
  test_species = c("human", "monkey", "rat", "mouse", "zebrafish", "fly"),
  method = c("homologene", "gprofiler", "babelgene"),
  make_plot = TRUE,
  show_plot = TRUE,
  verbose = TRUE
)
infer_species(
  gene_df,
  gene_input = "rownames",
  test_species = c("human", "monkey", "rat", "mouse", "zebrafish", "fly"),
  method = c("homologene", "gprofiler", "babelgene"),
  make_plot = TRUE,
  show_plot = TRUE,
  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"`.
`test_species`	Which species to test for matches with. If set to `NULL`, will default to a list of humans and 5 common model organisms. If `test_species` is set to one of the following options, it will automatically pull all species from that respective package and test against each of them: "homologene" : 20+ species (default) "gprofiler" : 700+ species "babelgene" : 19 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.
`make_plot`	Make a plot of the results.
`show_plot`	Print the plot of the results.
`verbose`	Print messages.

Value

An ordered dataframe of test_species from best to worst matches.

Examples

 
data("exp_mouse") 
matches <- orthogene::infer_species(gene_df = exp_mouse[1:200,])
data("exp_mouse") 
matches <- orthogene::infer_species(gene_df = exp_mouse[1:200,])

Map genes

Description

Input a list of genes, transcripts, proteins, SNPs, or genomic ranges in any format (HGNC, Ensembl, RefSeq, UniProt, etc.) and return a table with standardised gene symbols (the "names" column).

Usage

map_genes(
  genes,
  species = "hsapiens",
  target = "ENSG",
  mthreshold = Inf,
  drop_na = FALSE,
  numeric_ns = "",
  run_map_species = TRUE,
  verbose = TRUE
)
map_genes(
  genes,
  species = "hsapiens",
  target = "ENSG",
  mthreshold = Inf,
  drop_na = FALSE,
  numeric_ns = "",
  run_map_species = TRUE,
  verbose = TRUE
)

Arguments

`genes`	Gene list.
`species`	Species to map against.
`target`	target namespace.
`mthreshold`	maximum number of results per initial alias to show. Shows all by default.
`drop_na`	Drop all genes without mappings. Sets `gprofiler2::gconvert(filter_na=)` as well an additional round of more comprehensive `NA` filtering by orthogene.
`numeric_ns`	namespace to use for fully numeric IDs (list of available namespaces).
`run_map_species`	Standardise `species` names with map_species first (Default: `TRUE`).
`verbose`	Print messages.

Details

Uses gconvert. The exact contents of the output table will depend on target parameter. See ?gprofiler2::gconvert for more details.

Value

Table with standardised genes.

Examples

genes <- c(
    "Klf4", "Sox2", "TSPAN12", "NM_173007", "Q8BKT6",
    "ENSMUSG00000012396", "ENSMUSG00000074637"
)
mapped_genes <- map_genes(
    genes = genes,
    species = "mouse"
)
genes <- c(
    "Klf4", "Sox2", "TSPAN12", "NM_173007", "Q8BKT6",
    "ENSMUSG00000012396", "ENSMUSG00000074637"
)
mapped_genes <- map_genes(
    genes = genes,
    species = "mouse"
)

Map orthologs

Description

Map orthologs from one species to another.

Usage

map_orthologs(
  genes,
  standardise_genes = FALSE,
  input_species,
  output_species = "human",
  method = c("gprofiler", "homologene", "babelgene"),
  mthreshold = Inf,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  verbose = TRUE,
  ...
)
map_orthologs(
  genes,
  standardise_genes = FALSE,
  input_species,
  output_species = "human",
  method = c("gprofiler", "homologene", "babelgene"),
  mthreshold = Inf,
  gene_map = NULL,
  input_col = "input_gene",
  output_col = "ortholog_gene",
  verbose = TRUE,
  ...
)

Arguments

`genes`	can be a mixture of any format (HGNC, Ensembl, RefSeq, UniProt, etc.) and will be automatically converted to standardised HGNC symbol format.
`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.
`mthreshold`	Maximum number of ortholog names per gene to show. Passed to gorth. Only used when `method="gprofiler"` (DEFAULT : `Inf`).
`gene_map`	A data.frame that maps the current gene names to new gene names. This function's behaviour will adapt to different situations as follows: `gene_map=<data.frame>` : When a data.frame containing the gene key:value columns (specified by `input_col` and `output_col`, respectively) is provided, this will be used to perform aggregation/expansion. `gene_map=NULL` and `input_species!=output_species` : A `gene_map` is automatically generated by map_orthologs to perform inter-species gene aggregation/expansion. `gene_map=NULL` and `input_species==output_species` : A `gene_map` is automatically generated by map_genes to perform within-species gene gene symbol standardization and aggregation/expansion.
`input_col`	Column name within `gene_map` with gene names matching the row names of `X`.
`output_col`	Column name within `gene_map` with gene names that you wish you map the row names of `X` onto.
`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.

Details

map_orthologs() is a core function within convert_orthologs(), but does not have many of the extra checks, such as non121_strategy) and drop_nonorths.

Value

Ortholog map data.frame with at least the columns "input_gene" and "ortholog_gene".

Examples

data("exp_mouse")
gene_map <- map_orthologs(
    genes = rownames(exp_mouse),
    input_species = "mouse")
data("exp_mouse")
gene_map <- map_orthologs(
    genes = rownames(exp_mouse),
    input_species = "mouse")

Standardise species names

Description

Search gprofiler database for species that match the input text string. Then translate to a standardised species ID.

Usage

map_species(
  species = NULL,
  search_cols = c("display_name", "id", "scientific_name", "taxonomy_id"),
  output_format = c("scientific_name", "id", "display_name", "taxonomy_id", "version",
    "scientific_name_formatted"),
  method = c("homologene", "gprofiler", "babelgene"),
  remove_subspecies = TRUE,
  remove_subspecies_exceptions = c("Canis lupus familiaris"),
  use_local = TRUE,
  verbose = TRUE
)
map_species(
  species = NULL,
  search_cols = c("display_name", "id", "scientific_name", "taxonomy_id"),
  output_format = c("scientific_name", "id", "display_name", "taxonomy_id", "version",
    "scientific_name_formatted"),
  method = c("homologene", "gprofiler", "babelgene"),
  remove_subspecies = TRUE,
  remove_subspecies_exceptions = c("Canis lupus familiaris"),
  use_local = TRUE,
  verbose = TRUE
)

Arguments

`species`	Species query (e.g. "human", "homo sapiens", "hsapiens", or 9606). If given a list, will iterate queries for each item. Set to `NULL` to return all species.
`search_cols`	Which columns to search for `species` substring in metadata API.
`output_format`	Which column to return.
`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.
`remove_subspecies`	Only keep the first two taxonomic levels: e.g. "Canis lupus familiaris" –> "Canis lupus"
`remove_subspecies_exceptions`	Selected species to ignore when `remove_subspecies=TRUE`. e.g. "Canis lupus familiaris" –> "Canis lupus familiaris"
`use_local`	If `TRUE` default, map_species uses a locally stored version of the species metadata table instead of pulling directly from the gprofiler API. Local version may not be fully up to date, but should suffice for most use cases.
`verbose`	Print messages.

Value

Species ID of type output_format

Examples

ids <- map_species(species = c(
    "human", 9606, "mus musculus",
    "fly", "C elegans"
))
ids <- map_species(species = c(
    "human", 9606, "mus musculus",
    "fly", "C elegans"
))

Create a phylogenetic tree of shared orthologs

Description

Automatically creates a phylogenetic tree plot annotated with metadata describing how many orthologous genes each species shares with the reference_species ("human" by default).

Usage

plot_orthotree(
  tree = NULL,
  orth_report = NULL,
  species = NULL,
  method = c("babelgene", "homologene", "gprofiler"),
  tree_source = "timetree",
  non121_strategy = "drop_both_species",
  reference_species = "human",
  clades = list(Primates = c("Homo sapiens", "Macaca mulatta"), Eutherians =
    c("Homo sapiens", "Mus musculus", "Bos taurus"), Mammals = c("Homo sapiens",
    "Mus musculus", "Bos taurus", "Ornithorhynchus anatinus", "Monodelphis domestica"),
    Tetrapods = c("Homo sapiens", "Mus musculus", "Gallus gallus", "Anolis carolinensis",
    "Xenopus tropicalis"), Vertebrates = c("Homo sapiens", "Mus musculus",
    "Gallus gallus", "Anolis carolinensis", "Xenopus tropicalis", "Danio rerio"),
    Invertebrates = c("Drosophila melanogaster", 
     "Caenorhabditis elegans")),
  clades_rotate = list(),
  scaling_factor = NULL,
  show_plot = TRUE,
  save_paths = c(tempfile(fileext = ".ggtree.pdf"), tempfile(fileext = ".ggtree.png")),
  width = 15,
  height = width,
  mc.cores = 1,
  verbose = TRUE
)
plot_orthotree(
  tree = NULL,
  orth_report = NULL,
  species = NULL,
  method = c("babelgene", "homologene", "gprofiler"),
  tree_source = "timetree",
  non121_strategy = "drop_both_species",
  reference_species = "human",
  clades = list(Primates = c("Homo sapiens", "Macaca mulatta"), Eutherians =
    c("Homo sapiens", "Mus musculus", "Bos taurus"), Mammals = c("Homo sapiens",
    "Mus musculus", "Bos taurus", "Ornithorhynchus anatinus", "Monodelphis domestica"),
    Tetrapods = c("Homo sapiens", "Mus musculus", "Gallus gallus", "Anolis carolinensis",
    "Xenopus tropicalis"), Vertebrates = c("Homo sapiens", "Mus musculus",
    "Gallus gallus", "Anolis carolinensis", "Xenopus tropicalis", "Danio rerio"),
    Invertebrates = c("Drosophila melanogaster", 
     "Caenorhabditis elegans")),
  clades_rotate = list(),
  scaling_factor = NULL,
  show_plot = TRUE,
  save_paths = c(tempfile(fileext = ".ggtree.pdf"), tempfile(fileext = ".ggtree.png")),
  width = 15,
  height = width,
  mc.cores = 1,
  verbose = TRUE
)

Arguments

`tree`	A phylogenetic tree of class phylo. If no tree is provided (`NULL`) a 100-way multiz tree will be imported from UCSC Genome Browser.
`orth_report`	An ortholog report from one or more species generated by report_orthologs.
`species`	Species to include in the final plot. If `NULL`, then all species from the given database (`method`) will be included (via map_species), so long as they also exist in the `tree`.
`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.
`tree_source`	Can be one of the following: "timetree2022": Import and prune the TimeTree >147k species phylogenetic tree. Can also simply type "timetree". "timetree2015": Import and prune the TimeTree >50k species phylogenetic tree. "OmaDB": Construct a tree from OMA (Orthologous Matrix browser) via the getTaxonomy function. NOTE: Does not contain branch lengths, and therefore may have limited utility. "UCSC": Import and prune the UCSC 100-way alignment phylogenetic tree (hg38 version). "<path>": Read a tree from a newick text file from a local or remote URL using read.tree.
`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`.
`reference_species`	Reference species.
`clades`	A named list of clades each containing a character vector of species used to define the respective clade using MRCA.
`clades_rotate`	A list of clades to rotate (via rotate), each containing a character vector of species used to define the respective clade using MRCA.
`scaling_factor`	How much to scale y-axis parameters (e.g. offset) by.
`show_plot`	Whether to print the final tree plot.
`save_paths`	Paths to save plot to.
`width`	Saved plot width.
`height`	Saved plot height.
`mc.cores`	Number of cores to parallelise different steps with.
`verbose`	Print messages.

Value

A list containing:

plot : Annotated ggtree object.
tree : The pruned, standardised phylogenetic tree used in the plot.
orth_report : Ortholog reports for each species against the reference_species.
metadata : Metadata used in the plot, including silhouette PNG ids from phylopic.
clades : Metadata used for highlighting clades.
method : method used.
reference_species : reference_species used.
save_paths : save_paths to plot.

Source

ggtree tutorial

Examples

orthotree <- plot_orthotree(species = c("human","monkey","mouse"))
orthotree <- plot_orthotree(species = c("human","monkey","mouse"))

Prepare a phylogenetic tree

Description

Import a phylogenetic tree and then conduct a series of optional standardisation steps. Optionally, if output_format is not NULL, species names from both the tree and the species argument will first be standardised using map_species.

Usage

prepare_tree(
  tree_source = "timetree",
  species = NULL,
  output_format = "scientific_name_formatted",
  run_map_species = c(TRUE, TRUE),
  method = c("homologene", "gprofiler", "babelgene"),
  force_ultrametric = TRUE,
  age_max = NULL,
  show_plot = TRUE,
  save_dir = tools::R_user_dir("orthogene", which = "cache"),
  verbose = TRUE,
  ...
)
prepare_tree(
  tree_source = "timetree",
  species = NULL,
  output_format = "scientific_name_formatted",
  run_map_species = c(TRUE, TRUE),
  method = c("homologene", "gprofiler", "babelgene"),
  force_ultrametric = TRUE,
  age_max = NULL,
  show_plot = TRUE,
  save_dir = tools::R_user_dir("orthogene", which = "cache"),
  verbose = TRUE,
  ...
)

Arguments

`tree_source`	Can be one of the following: "timetree2022": Import and prune the TimeTree >147k species phylogenetic tree. Can also simply type "timetree". "timetree2015": Import and prune the TimeTree >50k species phylogenetic tree. "OmaDB": Construct a tree from OMA (Orthologous Matrix browser) via the getTaxonomy function. NOTE: Does not contain branch lengths, and therefore may have limited utility. "UCSC": Import and prune the UCSC 100-way alignment phylogenetic tree (hg38 version). "<path>": Read a tree from a newick text file from a local or remote URL using read.tree.
`species`	Species names to subset the tree by (after `standardise_species` step).
`output_format`	Which column to return.
`run_map_species`	Whether to first standardise species names with map_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_ultrametric`	Whether to force the tree to be ultrametric (i.e. make all tips the same date) using force.ultrametric.
`age_max`	Rescale the edges of the tree into units of millions of years (MY) instead than evolutionary rates (e.g. dN/dS ratios). Only used if `age_max`, the max number , is numeric. Times are computed using makeChronosCalib and chronos.
`show_plot`	Show a basic plot of the resulting tree.
`save_dir`	Directory to cache full tree in. Set to `NULL` to avoid using cache.
`verbose`	Print messages.
`...`	Additional arguments passed to makeChronosCalib.

Value

A filtered tree of class "phylo" (with standardised species names).

Source

TimeTree 5: An Expanded Resource for Species Divergence Times

Examples

species <- c("human","chimp","mouse")
tr <- orthogene::prepare_tree(species = species)
species <- c("human","chimp","mouse")
tr <- orthogene::prepare_tree(species = species)

Report orthologs

Description

Identify the number of orthologous genes between two species.

Usage

report_orthologs(
  target_species = "mouse",
  reference_species = "human",
  standardise_genes = FALSE,
  method_all_genes = c("homologene", "gprofiler", "babelgene"),
  method_convert_orthologs = method_all_genes,
  drop_nonorths = TRUE,
  non121_strategy = "drop_both_species",
  round_digits = 2,
  return_report = TRUE,
  ref_genes = NULL,
  mc.cores = 1,
  verbose = TRUE,
  ...
)
report_orthologs(
  target_species = "mouse",
  reference_species = "human",
  standardise_genes = FALSE,
  method_all_genes = c("homologene", "gprofiler", "babelgene"),
  method_convert_orthologs = method_all_genes,
  drop_nonorths = TRUE,
  non121_strategy = "drop_both_species",
  round_digits = 2,
  return_report = TRUE,
  ref_genes = NULL,
  mc.cores = 1,
  verbose = TRUE,
  ...
)

Arguments

`target_species`	Target species.
`reference_species`	Reference species.
`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.
`method_all_genes`	R package to to use in all_genes step: `"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.
`method_convert_orthologs`	R package to to use in convert_orthologs step: `"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`.
`round_digits`	Number of digits to round to when printing percentages.
`return_report`	Return just the ortholog mapping between two species (`FALSE`) or return both the ortholog mapping as well a `data.frame` of the report statistics (`TRUE`).
`ref_genes`	A table of all genes for the `reference_species`. If `NULL` (default), this will automatically be created using all_genes.
`mc.cores`	Number of cores to parallelise each `target_species` with.
`verbose`	Print messages.
`...`	Arguments passed on to `convert_orthologs` `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. `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. `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`). `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. `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"`). `sort_rows` Sort `gene_df` rows alphanumerically. `gene_map` A data.frame that maps the current gene names to new gene names. This function's behaviour will adapt to different situations as follows: `gene_map=<data.frame>` : When a data.frame containing the gene key:value columns (specified by `input_col` and `output_col`, respectively) is provided, this will be used to perform aggregation/expansion. `gene_map=NULL` and `input_species!=output_species` : A `gene_map` is automatically generated by map_orthologs to perform inter-species gene aggregation/expansion. `gene_map=NULL` and `input_species==output_species` : A `gene_map` is automatically generated by map_genes to perform within-species gene gene symbol standardization and aggregation/expansion. `as_DelayedArray` Convert aggregated matrix to DelayedArray. `input_col` Column name within `gene_map` with gene names matching the row names of `X`. `output_col` Column name within `gene_map` with gene names that you wish you map the row names of `X` onto.

Value

A list containing:

map : A table of inter-species gene mappings.
report : A list of aggregate orthology report statistics.

If >1 target_species are provided, then a table of aggregated report statistics concatenated across species will be returned instead.

Examples

orth_fly <- report_orthologs(
    target_species = "fly",
    reference_species = "human")
orth_fly <- report_orthologs(
    target_species = "fly",
    reference_species = "human")

Package 'orthogene'

Help Index

orthogene: Interspecies gene mapping

Description

Details

Author(s)

Source

See Also

Aggregate/expand a gene matrix by gene mappings

Description

Usage

Arguments

Value

Examples

Get all genes

Description

Usage

Arguments

Details

Value

Examples

All species

Description

Usage

Arguments

Value

Examples

Map genes from one species to another

Description

Usage

Arguments

Value

Examples

Create gene background

Description

Usage

Arguments

Value

Examples

Gene expression data: mouse

Description

Usage

Format

Source

Transcript expression data: mouse

Description

Usage

Format

Source

Format species names

Description

Usage

Arguments

Value

Examples

Get silhouettes

Description

Usage

Arguments

Value

Source

Examples

gconvert namespaces

Description

Format

Source

Reference organisms

Description

Format

Source

Infer species from gene names

Description

Usage

Arguments

Value

Examples

Map genes

Description

Usage

Arguments