Package 'fenr' reference manual

Title:	Fast functional enrichment for interactive applications
Description:	Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages.
Authors:	Marek Gierlinski [aut, cre]
Maintainer:	Marek Gierlinski <[email protected]>
License:	MIT + file LICENSE
Version:	1.5.0
Built:	2024-12-10 06:16:02 UTC
Source:	https://github.com/bioc/fenr

Small Shiny app serving as example for fast enrichment

Description

Small Shiny app serving as example for fast enrichment

Usage

enrichment_interactive(de, term_data)
enrichment_interactive(de, term_data)

Arguments

`de`	Differential expression results, `yeast_de` attached to this package can be used.
`term_data`	A list of `fenr_terms` objects containing functional data for various ontologies. `fetch_terms_for_example` can be used to create this object.

Value

An interactive Shiny app

Examples

## Not run: 
data(yeast_de)
term_data <- fetch_terms_for_example(yeast_de)
  enrichment_interactive(yeast_de, term_data)

## End(Not run)
## Not run: 
data(yeast_de)
term_data <- fetch_terms_for_example(yeast_de)
  enrichment_interactive(yeast_de, term_data)

## End(Not run)

Example set of background genes.

Description

A set of gene names for proteins that could be detected in a typical proteomics experiment on yeast samples.

Usage

data(exmpl_all)
data(exmpl_all)

Format

A character vector with 6985 elements.

Value

A vector with background gene names.

Example set of selected genes.

Description

A set of gene names manually selected to illustrate functional enrichment.

Usage

data(exmpl_sel)
data(exmpl_sel)

Format

A character vector with 21 elements.

Value

A vector with selected gene names.

Get functional term data from BioPlanet

Description

Download term information (term ID and name) and gene-pathway mapping (NCBI gene ID, gene symbol and pathway ID) from BioPlanet.

Usage

fetch_bp(use_cache = TRUE, on_error = c("stop", "warn", "ignore"))
fetch_bp(use_cache = TRUE, on_error = c("stop", "warn", "ignore"))

Arguments

`use_cache`	Logical, if TRUE, the remote file will be cached locally.
`on_error`	A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Value

A list with terms and mapping tibbles.

Examples

bioplanet_data <- fetch_bp(on_error = "warn")
bioplanet_data <- fetch_bp(on_error = "warn")

Get Gene Ontology (GO) data

Description

This function downloads term information (GO term ID and name) and gene-term mapping (gene ID, symbol, and GO term ID) from either the Ensembl database (using BioMart) or the Gene Ontology database (using GAF files), depending on the provided argument.

Usage

fetch_go(
  species = NULL,
  dataset = NULL,
  use_cache = TRUE,
  on_error = c("stop", "warn", "ignore")
)
fetch_go(
  species = NULL,
  dataset = NULL,
  use_cache = TRUE,
  on_error = c("stop", "warn", "ignore")
)

Arguments

`species`	(Optional) Species designation. Examples are `goa_human` for human, `mgi` for mouse, or `sgd` for yeast. Full list of available species can be obtained using `fetch_go_species` - column `designation`. This argument is used when fetching data from the Gene Ontology database.
`dataset`	(Optional) A string representing the dataset passed to Ensebml's Biomart, e.g. 'scerevisiae_gene_ensembl'. To see the different datasets available within a biomaRt you can e.g. do: mart <- biomaRt::useEnsembl(biomart = "ensembl"), followed by biomaRt::listDatasets(mart).
`use_cache`	Logical, if TRUE, the remote data will be cached locally.
`on_error`	A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Details

If species is provided, mapping from a Gene Ontology GAF file will be downloaded. GAF files contain more generic information than gene symbols. In this function, the third column of the GAF file (DB Object Symbol) is returned as gene_symbol, but, depending on the species argument it can contain other entities, e.g. RNA or protein complex names. Similarly, the eleventh column of the GAF file (DB Object Synonym) is returned as gene_id. It is up to the user to select the appropriate database.

Alternatively, if dataset is provided, mapping will be downloaded from Ensembl database. It will gene symbol and Ensembl gene ID.

Value

A list with terms and mapping tibbles.

Examples

# Fetch GO data from Ensembl
go_data_ensembl <- fetch_go(dataset = "scerevisiae_gene_ensembl", on_error = "warn")
# Fetch GO data from Gene Ontology
go_data_go <- fetch_go(species = "sgd", on_error = "warn")
# Fetch GO data from Ensembl
go_data_ensembl <- fetch_go(dataset = "scerevisiae_gene_ensembl", on_error = "warn")
# Fetch GO data from Gene Ontology
go_data_go <- fetch_go(species = "sgd", on_error = "warn")

Find all species available from geneontology.org

Description

This function attempts to scrape HTML web page containing a table of available species and corresponding file names. If the structure of the page changes one day and the function stops working, go to http://current.geneontology.org/products/pages/downloads.html and check file names. The species designation used in this package is the GAF file name without extension (e.g. for a file ‘goa_chicken.gaf’ the designation is ‘goa_chicken’).

Usage

fetch_go_species(on_error = c("stop", "warn", "ignore"))
fetch_go_species(on_error = c("stop", "warn", "ignore"))

Arguments

on_error

A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Value

A tibble with columns species and designation.

Examples

go_species <- fetch_go_species(on_error = "warn")
go_species <- fetch_go_species(on_error = "warn")

Get functional term data from KEGG

Description

Download information (pathway ID and name) and gene-pathway mapping (entrez gene ID, gene symbol and pathway ID) from KEGG. Gene symbols are extracted from gene descriptions. For some species (e.g. yeast), gene symbols are returned instead of entrez IDs and not in gene description.

Usage

fetch_kegg(species, batch_size = 10, on_error = c("stop", "warn", "ignore"))
fetch_kegg(species, batch_size = 10, on_error = c("stop", "warn", "ignore"))

Arguments

`species`	KEGG species code, for example "hsa" for human. The full list of available KEGG species can be found by using `fetch_kegg_species`.
`batch_size`	Number of pathways sent to KEGG database in one query. The maximum allowed is 10.
`on_error`	A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Value

A list with terms and mapping tibbles.

Examples

kegg_data <- fetch_kegg("mge", on_error = "warn")
kegg_data <- fetch_kegg("mge", on_error = "warn")

Find all species available from KEGG

Description

Find all species available from KEGG

Usage

fetch_kegg_species(on_error = c("stop", "warn", "ignore"))
fetch_kegg_species(on_error = c("stop", "warn", "ignore"))

Arguments

on_error

Value

A tibble, in which column designation contains species designations used in function fetch_kegg.

Examples

spe <- fetch_kegg_species(on_error = "warn")
spe <- fetch_kegg_species(on_error = "warn")

Get functional term data from Reactome

Description

Download term information (pathway ID and name) and gene-pathway mapping (Ensembl gene ID or gene symbol and pathway ID) from Reactome.

Usage

fetch_reactome(
  species,
  source = c("ensembl", "api", "gene_association"),
  use_cache = TRUE,
  on_error = c("stop", "warn", "ignore")
)
fetch_reactome(
  species,
  source = c("ensembl", "api", "gene_association"),
  use_cache = TRUE,
  on_error = c("stop", "warn", "ignore")
)

Arguments

`species`	Reactome species designation, for example "Homo sapiens" for human. Full list of available species can be found using `fetch_reactome_species()`.
`source`	How to download the mapping. If 'ensembl' or 'gene_association', one mapping file provided by Reactome will be downloaded, if 'api', then Reactome API will be used. See details.
`use_cache`	Logical, if TRUE, the remote file will be cached locally.
`on_error`	A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Details

Reactome makes mapping between Ensembl ID and pathway ID available in form of one downloadable file. This mapping contains gene symbols as well. Also, a gene association file with mapping between UniProt accession number, gene symbol and Reactome term is available. If source = "ensembl" or source = "gene_association" is set, one large file will be downloaded and parsed. If source = "api" is set, then Reactome APIs will be interrogated for each pathway available. This method is considerably slower, especially for large genomes. However, gene association file contains far fewer mappings than can be extracted using API. If gene symbols are needed, we recommend using source = "ensembl".

Value

A list with terms and mapping tibbles

Examples

reactome_data <- fetch_reactome("Saccharomyces cerevisiae", on_error = "warn")
reactome_data <- fetch_reactome("Saccharomyces cerevisiae", on_error = "warn")

List of available Reactome species

Description

List of available Reactome species

Usage

fetch_reactome_species(on_error = c("stop", "warn", "ignore"))
fetch_reactome_species(on_error = c("stop", "warn", "ignore"))

Arguments

on_error

Value

A tibble with species names used by Reactome.

Examples

re <- fetch_reactome_species(on_error = "warn")
re <- fetch_reactome_species(on_error = "warn")

Create term data for interactive example

Description

Create term data for interactive example

Usage

fetch_terms_for_example(de)
fetch_terms_for_example(de)

Arguments

`de`	Differential expression results, use `yeast_de` data attached to this package.

Value

A list of objects containing functional terms for GO and Reactome.

Examples

## Not run: 
data(yeast_de)
term_data <- fetch_terms_for_example(yeast_de)

## End(Not run)
## Not run: 
data(yeast_de)
term_data <- fetch_terms_for_example(yeast_de)

## End(Not run)

Get functional term data from WikiPathways

Description

Download term information (pathway ID and name) and gene-pathway mapping (gene symbol and pathway ID) from WikiPathways.

Usage

fetch_wiki(
  species,
  databases = c("Ensembl", "Entrez Gene", "HGNC", "HGNC Accession number",
    "Uniprot-TrEMBL"),
  types = c("GeneProduct", "Protein", "Rna", "RNA"),
  on_error = c("stop", "warn", "ignore")
)
fetch_wiki(
  species,
  databases = c("Ensembl", "Entrez Gene", "HGNC", "HGNC Accession number",
    "Uniprot-TrEMBL"),
  types = c("GeneProduct", "Protein", "Rna", "RNA"),
  on_error = c("stop", "warn", "ignore")
)

Arguments

`species`	WikiPathways species designation, for example "Homo sapiens" for human. Full list of available species can be found using `fetch_wiki_species()`.
`databases`	A character vector with database names to pre-filter mapping data. See details. Full result will be returned if NULL.
`types`	A character vector with types of entities to pre-filter mapping data. See details. Full result will be returned if NULL.
`on_error`	A character string indicating the error handling strategy: either "stop" to halt execution, "warn" to issue a warning and return 'NULL' or "ignore" to return 'NULL' without warnings. Defaults to "stop".

Details

WikiPathways contain mapping between pathways and a variety of entities from various databases. Typically a gene symbol is returned in column text_label and some sort of ID in column id, but this depends on the species and databases used. For gene/protein enrichment, these should be filtered to contain gene symbols only. This can be done by selecting a desired databases and types. The default values for parameters databases and types attempt to select information from generic databases, but there are organism-specific databases not included in the selection. We suggest to run this function with databases = NULL, types = NULL to see what types and databases are available before making selection.

Value

A list with terms and mapping tibbles.

Examples

wiki_data <- fetch_wiki("Bacillus subtilis", on_error = "warn")
wiki_data <- fetch_wiki("Bacillus subtilis", on_error = "warn")

List of available WikiPathways species

Description

List of available WikiPathways species

Usage

fetch_wiki_species(on_error = c("stop", "warn", "ignore"))
fetch_wiki_species(on_error = c("stop", "warn", "ignore"))

Arguments

on_error

Value

A character vector with species names used by WikiPathways.

Examples

spec <- fetch_wiki_species(on_error = "warn")
spec <- fetch_wiki_species(on_error = "warn")

Fast Functional Enrichment

Description

Perform fast functional enrichment analysis based on the hypergeometric distribution. Designed for use in interactive applications.

Usage

functional_enrichment(feat_all, feat_sel, term_data, feat2name = NULL)
functional_enrichment(feat_all, feat_sel, term_data, feat2name = NULL)

Arguments

`feat_all`	A character vector with all feature identifiers, serving as the background for enrichment.
`feat_sel`	A character vector with feature identifiers in the selection.
`term_data`	An object of class `fenr_terms`, created by `prepare_for_enrichment`.
`feat2name`	An optional named list to convert feature IDs into feature names.

Details

This function carries out functional enrichment analysis on a selection of features (e.g., differentially expressed genes) using the hypergeometric probability distribution (Fisher's exact test). Features can be genes, proteins, etc. The term_data object contains functional term information and feature-term mapping.

Value

A tibble with enrichment results, providing the following information for each term:

N_with - number of features with this term among all features
n_with_sel - number of features with this term in the selection
n_expect - expected number of features with this term in the selection, under the null hypothesis that terms are mapped to features randomly
enrichment - ratio of n_with_sel / n_expect
odds_ratio - odds ratio for enrichment; is infinite when all features with the given term are in the selection
p_value - p-value from a single hypergeometric test
p_adjust - p-value adjusted for multiple tests using the Benjamini-Hochberg approach

Examples

## Not run: 
data(exmpl_all, exmpl_sel)
go <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go$terms, go$mapping, exmpl_all, feature_name = "gene_symbol")
enr <- functional_enrichment(exmpl_all, exmpl_sel, go_terms)

## End(Not run)
## Not run: 
data(exmpl_all, exmpl_sel)
go <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go$terms, go$mapping, exmpl_all, feature_name = "gene_symbol")
enr <- functional_enrichment(exmpl_all, exmpl_sel, go_terms)

## End(Not run)

Get terms annotating a given feature.

Description

Get terms annotating a given feature.

Usage

get_feature_terms(term_data, feature_id)
get_feature_terms(term_data, feature_id)

Arguments

`term_data`	An object class `fenr_terms`, created by `prepare_for_enrichment`.
`feature_id`	A string with a feature ID

Value

A character vector containing functional term IDs annotating given feature.

Examples

## Not run: 
go_data <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go_data$terms, go_data$mapping, feature = "gene_symbol")
trms <- get_feature_terms(go_terms, "GEM1")

## End(Not run)
## Not run: 
go_data <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go_data$terms, go_data$mapping, feature = "gene_symbol")
trms <- get_feature_terms(go_terms, "GEM1")

## End(Not run)

Get features annotated with a given term.

Description

Get features annotated with a given term.

Usage

get_term_features(term_data, term_id)
get_term_features(term_data, term_id)

Arguments

`term_data`	An object class `fenr_terms`, created by `prepare_for_enrichment`.
`term_id`	A string with a functional term ID.

Value

A character vector containing feature IDs annotated with the term ID.

Examples

## Not run: 
go_data <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go_data$terms, go_data$mapping, feature = "gene_symbol")
feats <- get_term_features(go_terms, "GO:0000001")

## End(Not run)
## Not run: 
go_data <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go_data$terms, go_data$mapping, feature = "gene_symbol")
feats <- get_term_features(go_terms, "GO:0000001")

## End(Not run)

GO-terms data downloaded for the vignette.

Description

Downloaded using go <- fetch_go(species = "sgd")

Usage

data(go)
data(go)

Format

A list of two tibbles

Value

Contains GO-term descriptions and gene mapping.

GO species

Description

Downloaded using go_species <- fetch_go_species()

Usage

data(go_species)
data(go_species)

Format

A tibble

Value

Contains species available through Gene Ontology

Prepare Term Data for Enrichment Analysis

Description

Process term data downloaded with the fetch_* functions, preparing it for fast enrichment analysis using functional_enrichment.

Usage

prepare_for_enrichment(
  terms,
  mapping,
  all_features = NULL,
  feature_name = "gene_id"
)
prepare_for_enrichment(
  terms,
  mapping,
  all_features = NULL,
  feature_name = "gene_id"
)

Arguments

`terms`	A tibble with at least two columns: `term_id` and `term_name`. This tibble contains information about functional term names and descriptions.
`mapping`	A tibble with at least two columns, containing the mapping between functional terms and features. One column must be named `term_id`, while the other column should have a name specified by the `feature_name` argument. For example, if `mapping` contains columns `term_id`, `accession_number`, and `gene_symbol`, setting `feature_name = "gene_symbol"` indicates that gene symbols will be used for enrichment analysis.
`all_features`	A vector with all feature IDs used as the background for enrichment. If not specified, all features found in `mapping` will be used, resulting in a larger object size.
`feature_name`	The name of the column in the `mapping` tibble to be used as the feature identifier. For example, if `mapping` contains columns `term_id`, `accession_number`, and `gene_symbol`, setting `feature_name = "gene_symbol"` indicates that gene symbols will be used for enrichment analysis.

Details

This function takes two tibbles containing functional term information (terms) and feature mapping (mapping), and converts them into an object required by functional_enrichment for efficient analysis. Terms and mapping can be generated with the database access functions included in this package, such as fetch_reactome or fetch_go_from_go.

Value

An object of class fenr_terms required by functional_enrichment.

Examples

## Not run: 
data(exmpl_all)
go <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go$terms, go$mapping, exmpl_all,
                                   feature_name = "gene_symbol")

## End(Not run)
## Not run: 
data(exmpl_all)
go <- fetch_go(species = "sgd")
go_terms <- prepare_for_enrichment(go$terms, go$mapping, exmpl_all,
                                   feature_name = "gene_symbol")

## End(Not run)

Remove all cache

Description

This function will remove all cached data used by 'fenr'. The user will be prompted for confirmation. Use only when you suspect the cache was corrupted. Use with caution!

Usage

remove_cache(ask = TRUE)
remove_cache(ask = TRUE)

Arguments

ask

Logical, whether to ask user for confirmation.

Value

TRUE if successfully removed.

Examples

## Not run: 
remove_cache()

## End(Not run)
## Not run: 
remove_cache()

## End(Not run)

Differential expression results for yeast RNA-seq.

Description

A subset of 6 + 6 replicates was selected from data set reported in https://doi.org/10.1093/bioinformatics/btv425

Usage

data(yeast_de)
data(yeast_de)

Format

A tibble with 5 columns

Value

Results for differential expression for yeast RNA-seq.

Package 'fenr'

Help Index

Small Shiny app serving as example for fast enrichment

Description

Usage

Arguments

Value

Examples

Example set of background genes.

Description

Usage

Format

Value

Example set of selected genes.

Description

Usage

Format

Value

Get functional term data from BioPlanet

Description

Usage

Arguments

Value

Examples

Get Gene Ontology (GO) data

Description

Usage

Arguments

Details

Value

Examples

Find all species available from geneontology.org

Description

Usage

Arguments

Value

Examples

Get functional term data from KEGG

Description

Usage

Arguments

Value

Examples

Find all species available from KEGG

Description

Usage

Arguments

Value

Examples

Get functional term data from Reactome

Description

Usage

Arguments

Details

Value

Examples

List of available Reactome species

Description

Usage

Arguments

Value

Examples

Create term data for interactive example

Description

Usage

Arguments

Value

Examples

Get functional term data from WikiPathways

Description

Usage

Arguments

Details

Value

Examples

List of available WikiPathways species

Description

Usage

Arguments

Value