OmnipathR

https://r.omnipathdb.org/
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OmnipathR is an R client for the OmniPath web service that provides access to protein-protein...
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### Example: Browse OmnipathR Log (Non-runnable)

Source: https://r.omnipathdb.org/reference/omnipath_log.html

This example demonstrates how to call `omnipath_log()` within an `if (FALSE)` block, indicating it's a non-executable example for documentation purposes. It shows the basic usage for opening the log file.

```R
if (FALSE) { # \dontrun{
omnipath_log()
# then you can browse the log file, and exit with `q`
} # }
```

--------------------------------

### Install Nichenetr Package

Source: https://r.omnipathdb.org/articles/nichenet.html

Install the nichenetr package and its dependencies from GitHub. This function also installs necessary dependencies.

```r
require(devtools)
install_github('saeyslab/nichenetr')
```

--------------------------------

### Example of GO slim annotation output

Source: https://r.omnipathdb.org/reference/go_annot_slim.html

This example demonstrates how to call the `go_annot_slim` function and print the resulting tibble, showing gene annotations with GO slim terms.

```r
if (FALSE) {
goslim <- go_annot_slim(organism = 'human', slim = 'generic')
goslim
# # A tibble: 276,371 x 8
#    db     db_object_id db_object_symbol go_id aspect db_object_name
#    <fct>  <chr>        <chr>            <chr> <fct>  <chr>
#  1 UniPr. A0A024RBG1   NUDT4B           GO:0. F      Diphosphoinosito.
#  2 UniPr. A0A024RBG1   NUDT4B           GO:0. F      Diphosphoinosito.
#  3 UniPr. A0A024RBG1   NUDT4B           GO:0. C      Diphosphoinosito.
#  4 UniPr. A0A024RBG1   NUDT4B           GO:0. C      Diphosphoinosito.
#  5 UniPr. A0A024RBG1   NUDT4B           GO:0. C      Diphosphoinosito.
# # . with 276,366 more rows, and 2 more variables:
# #   db_object_synonym <chr>, db_object_type <fct>
}
```

--------------------------------

### Example Usage of remap_tf_target_download

Source: https://r.omnipathdb.org/reference/remap_tf_target_download.html

This example demonstrates how to call the `remap_tf_target_download` function and inspect the resulting data frame, which contains TF-target relationships with associated scores.

```R
if (FALSE) {
remap_interactions <- remap_tf_target_download()
remap_interactions
# # A tibble: 9,546,470 x 4
#    source_genesymbol target_genesymbol target_ensembl     score
#    <chr>             <chr>             <chr>              <dbl>
#  1 ADNP              PTPRS             ENSG00000105426.16  1000
#  2 AFF4              PRKCH             ENSG00000027075.14  1000
#  3 AHR               CTNND2            ENSG00000169862.18  1000
#  4 AR                PDE4D             ENSG00000113448.18  1000
#  5 ARID1A            PLEC              ENSG00000178209.14  1000
# # . with 9,546,460 more rows
}
```

--------------------------------

### Example Usage of Nichenet Ligand Activities

Source: https://r.omnipathdb.org/reference/nichenet_ligand_activities.html

This example demonstrates how to use the nichenet_ligand_activities function within a typical NicheNet analysis workflow. It shows the necessary preceding steps to generate the required input matrices and vectors.

```R
if (FALSE) {
networks <- nichenet_networks()
expression <- nichenet_expression_data()
optimization_results <- nichenet_optimization(networks, expression)
nichenet_model <- nichenet_build_model(optimization_results, networks)
lt_matrix <- nichenet_ligand_target_matrix(
    nichenet_model$weighted_networks,
    networks$lr_network,
    nichenet_model$optimized_parameters
)
ligand_activities <- nichenet_ligand_activities(
    ligand_target_matrix = lt_matrix,
    lr_network = networks$lr_network,
    # the rest of the parameters should come
    # from your transcriptomics data:
    expressed_genes_transmitter = expressed_genes_transmitter,
    expressed_genes_receiver = expressed_genes_receiver,
    genes_of_interest = genes_of_interest
)
}
```

--------------------------------

### Example Usage of get_signed_ptms

Source: https://r.omnipathdb.org/reference/get_signed_ptms.html

This example demonstrates how to use the get_signed_ptms function. It first imports enzyme-substrate data from 'PhosphoSite' and 'SIGNOR' resources, along with general interactions, and then uses these to generate signed PTMS.

```r
enzsub <- import_omnipath_enzsub(resources = c('PhosphoSite', 'SIGNOR'))
interactions <- import_omnipath_interactions()
enzsub <- get_signed_ptms(enzsub, interactions)
```

--------------------------------

### Test NicheNet Pipeline

Source: https://r.omnipathdb.org/articles/nichenet.html

Run a test of the NicheNet pipeline with a reduced set of parameters and dummy gene sets to verify installation and environment setup. This test takes approximately 10 minutes.

```r
require(OmnipathR)
nichenet_workarounds()
nichenet_test()
```

--------------------------------

### Example: Get InBioMap interaction data

Source: https://r.omnipathdb.org/reference/inbiomap_raw.html

This is an example of how to call the `inbiomap_raw` function to obtain the interaction data. The code is set to not run by default.

```R
if (FALSE) {
inbiomap_psimitab <- inbiomap_raw()
}
```

--------------------------------

### guide2pharma_download()

Source: https://r.omnipathdb.org/reference/index.html

Downloads interactions from the Guide to Pharmacology database.

```APIDOC
## guide2pharma_download()

### Description
Downloads interactions from the Guide to Pharmacology database

### Method
Function Call

### Parameters
None explicitly documented.

### Response
Interaction data from the Guide to Pharmacology database.
```

--------------------------------

### Install OmnipathR Package

Source: https://r.omnipathdb.org/articles/bioc_workshop.html

Installs the latest version of the OmnipathR package from GitHub. Ensure you have the devtools package installed.

```R
library(devtools)
install_github('saezlab/OmnipathR')
```

--------------------------------

### Example of calling NicheNet workarounds

Source: https://r.omnipathdb.org/reference/nichenet_workarounds.html

This is an example of how to call the `nichenet_workarounds` function. It is wrapped in `if (FALSE)` to prevent execution during standard package checks.

```R
if (FALSE) {
nichenet_workarounds()
}
```

--------------------------------

### guide2pharma_download

Source: https://r.omnipathdb.org/reference/guide2pharma_download.html

Downloads ligand-receptor interactions from the Guide to Pharmacology (IUPHAR/BPS) database.

```APIDOC
## guide2pharma_download

### Description
Downloads ligand-receptor interactions from the Guide to Pharmacology (IUPHAR/BPS) database (https://www.guidetopharmacology.org/).

### Usage
```R
guide2pharma_download()
```

### Value
A tibble (data frame) of interactions as it is provided by the database.

### Examples
```R
g2p_data <- guide2pharma_download()
g2p_data
```
```

--------------------------------

### Download Interactions from Guide to Pharmacology

Source: https://r.omnipathdb.org/reference/guide2pharma_download.html

Downloads ligand-receptor interactions from the Guide to Pharmacology database. Returns a tibble of interactions.

```r
guide2pharma_download()
```

```r
g2p_data <- guide2pharma_download()

g2p_data
```

--------------------------------

### Install OmnipathR Development Version

Source: https://r.omnipathdb.org/index.html

Installs the development version of OmnipathR directly from GitHub using the devtools package. This provides access to the latest features and updates.

```r
require(devtools)
install_github('saezlab/OmnipathR')
```

--------------------------------

### nichenet_lr_network_guide2pharma

Source: https://r.omnipathdb.org/reference/nichenet_lr_network_guide2pharma.html

Downloads ligand-receptor interactions from the Guide to Pharmacology database and converts it to a format suitable for NicheNet.

```APIDOC
## nichenet_lr_network_guide2pharma

### Description
Downloads ligand-receptor interactions from the Guide to Pharmacology database and converts it to a format suitable for NicheNet.

### Usage
```R
nichenet_lr_network_guide2pharma()
```

### Value
Data frame with ligand-receptor interactions in NicheNet format.

### See also
`nichenet_lr_network`, `guide2pharma_download`

### Examples
```R
g2p_lr_network <- nichenet_lr_network_guide2pharma()
```
```

--------------------------------

### Download, cache, and remove a specific resource

Source: https://r.omnipathdb.org/reference/omnipath_cache_remove.html

This example demonstrates downloading a resource from a URL, caching it, and then removing the specific cache entry using its key. It ensures the resource is available and then cleans up the cache.

```R
bioc_url <- 'https://bioconductor.org/'
version <- omnipath_cache_latest_or_new(url = bioc_url)
httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE))
#> Response [https://bioconductor.org/]
#>   Date: 2024-04-07 15:20
#>   Status: 200
#>   Content-Type: text/html
#>   Size: 37.3 kB
#> <ON DISK>  /home/denes/.cache/OmnipathR/41346a00fb20d2a9df03aa70cf4d50bf88ab154a-1
omnipath_cache_download_ready(version)
key <- omnipath_cache_key(bioc_url)
omnipath_cache_remove(key = key)
```

--------------------------------

### Example: Get Human Swiss-Prot Entries

Source: https://r.omnipathdb.org/reference/all_uniprots.html

Demonstrates fetching only the 'id' field for human reviewed UniProt entries. The output is a tibble showing entry names.

```r
human_swissprot_entries <- all_uniprots(fields = 'id')
human_swissprot_entries
#> # A tibble: 20,426 × 1
#>    `Entry Name`
#>    <chr>       
#>  1 CP2D7_HUMAN 
#>  2 PIOS1_HUMAN 
#>  3 SIK1B_HUMAN 
#>  4 MOTSC_HUMAN 
#>  5 CD3CH_HUMAN 
#>  6 NBDY_HUMAN  
#>  7 CIROP_HUMAN 
#>  8 S12A8_HUMAN 
#>  9 RBM47_HUMAN 
#> 10 IFT56_HUMAN 
#> # ℹ 20,416 more rows
# # A tibble: 20,396 x 1
#>    `Entry name`
#>    <chr>
#>  1 OR4K3_HUMAN
#>  2 O52A1_HUMAN
#>  3 O2AG1_HUMAN
#>  4 O10S1_HUMAN
#>  5 O11G2_HUMAN
#> # . with 20,386 more rows
```

--------------------------------

### Parse OBO File and Inspect Results

Source: https://r.omnipathdb.org/reference/obo_parser.html

Demonstrates downloading a GO slim OBO file, parsing it with `obo_parser` into a list format, and inspecting the structure of the parsed data.

```R
goslim_url <-
    "http://current.geneontology.org/ontology/subsets/goslim_generic.obo"
path <- tempfile()
httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE))
#> Response [http://current.geneontology.org/ontology/subsets/goslim_generic.obo]
#>   Date: 2024-04-07 15:00
#>   Status: 200
#>   Content-Type: text/obo
#>   Size: 115 kB
#> <ON DISK>  /tmp/RtmpgsvP1J/file165a6038fedfc0
obo <- obo_parser(path, tables = FALSE)
unlink(path)
names(obo)
#> [1] "names"       "namespaces"  "relations"   "subsets"     "obsolete"   
#> [6] "rel_lst_c2p"
```

```R
# [1] "names"      "namespaces" "relations"  "subsets"    "obsolete"
head(obo$relations, n = 2)
#> $`GO:0000228`
#> $`GO:0000228`$is_a
#> [1] "GO:0005694"
#> 
#> $`GO:0000228`$part_of
#> [1] "GO:0005634"
#> 
#> 
#> $`GO:0000278`
#> $`GO:0000278`$has_part
#> [1] "GO:0140014"
#> 
#> 
# $`GO:0000001`
# $`GO:0000001`$is_a
# [1] "GO:0048308" "GO:0048311"
#
# $`GO:0000002`
# $`GO:0000002`$is_a
# [1] "GO:0007005"
```

--------------------------------

### Generate Gene-Metabolite Network

Source: https://r.omnipathdb.org/reference/chalmers_gem_network.html

Call the function with default arguments to generate a gene-metabolite network for human. Note: This example may produce an error if the 'R.matlab' package is not installed.

```R
gem <- chalmers_gem_network()
#> Error in map2(.x, .y, .f, ..., .progress = .progress): ℹ In index: 1.
#> ℹ With name: col_types.
#> Caused by error in `loadNamespace()`:
#> ! there is no package called ‘R.matlab’
```

--------------------------------

### NicheNet ligand-target matrix creation

Source: https://r.omnipathdb.org/reference/nichenet_ligand_target_matrix.html

This example demonstrates the full workflow of building a NicheNet model and then using it to create the ligand-target matrix. It requires prior steps of fetching networks, expression data, optimizing parameters, and building the model.

```R
if (FALSE) {
networks <- nichenet_networks()
expression <- nichenet_expression_data()
optimization_results <- nichenet_optimization(networks, expression)
nichenet_model <- nichenet_build_model(optimization_results, networks)
lt_matrix <- nichenet_ligand_target_matrix(
    nichenet_model$weighted_networks,
    networks$lr_network,
    nichenet_model$optimized_parameters
)
}
```

--------------------------------

### Retrieve STITCH Links Dataset

Source: https://r.omnipathdb.org/reference/stitch_links.html

Call the stitch_links function to retrieve the default STITCH links dataset for humans. No specific setup or imports are required beyond having the package installed.

```R
stl <- stitch_links()

```

--------------------------------

### Get ReMap interactions with a different target count

Source: https://r.omnipathdb.org/reference/remap_filtered.html

This example demonstrates how to use `remap_filtered` to retrieve ReMap TF-target interactions, specifically limiting the number of targets per TF to 100. The output is a tibble showing source and target gene symbols.

```R
remap_interactions <- remap_filtered(top_targets = 100)
remap_interactions
```

--------------------------------

### Get Ancestors of Ontology Terms

Source: https://r.omnipathdb.org/reference/ancestors.html

Retrieves all ancestors (parents) of the specified ontology terms by recursively walking the ontology tree up to the root. It collects all visited nodes, which represent the ancestors of the starting nodes. The function can return either term IDs or names and allows filtering by specific relation types.

```APIDOC
## ancestors

### Description
Starting from the selected nodes, recursively walks the ontology tree until it reaches the root. Collects all visited nodes, which are the ancestors (parents) of the starting nodes.

### Usage
```R
ancestors(
  terms,
  db_key = "go_basic",
  ids = TRUE,
  relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates",
    "negatively_regulates")
)
```

### Arguments 

*   **terms** (Character vector) - Ontology term IDs or names. A mixture of IDs and names can be provided.
*   **db_key** (Character) - Key to identify the ontology database. For available keys see `omnipath_show_db`.
*   **ids** (Logical) - Whether to return IDs or term names.
*   **relations** (Character vector) - Ontology relation types. Only these relations will be used.

### Value
Character vector of ontology IDs. If the input terms are all root nodes, `NULL` is returned. The starting nodes won't be included in the result unless some of them are ancestors of other starting nodes.

### Details
Note: this function relies on the database manager, the first call might take long because of the database load process. Subsequent calls within a short period should be faster. See `get_ontology_db`.

### Examples
```R
ancestors('GO:0005035', ids = FALSE)
#> [1] "molecular_function"                       
#> [2] "transmembrane signaling receptor activity"
#> [3] "death receptor activity"                  
#> [4] "signaling receptor activity"              
#> [5] "molecular transducer activity"            
# [1] "molecular_function"
# [2] "transmembrane signaling receptor activity"
# [3] "signaling receptor activity"
# [4] "molecular transducer activity"
```
```

--------------------------------

### Save and Load HTML Content from Cache

Source: https://r.omnipathdb.org/reference/omnipath_cache_save.html

This example shows how to save HTML content downloaded from a URL to the cache and then load it back. It uses `readChar` to read the content and `url` to establish the connection.

```R
bioc_url <- 'https://bioconductor.org/'
bioc_html <- readChar(url(bioc_url), nchars = 99999)
omnipath_cache_save(bioc_html, url = bioc_url)
bioc_html <- omnipath_cache_load(url = bioc_url)
```

--------------------------------

### Install OmnipathR from Bioconductor

Source: https://r.omnipathdb.org/index.html

Installs the latest stable release of the OmnipathR package from Bioconductor. Ensure BiocManager is installed first.

```r
if (!requireNamespace(‘BiocManager’, quietly = TRUE)) install.packages(‘BiocManager’)

## Last release in Bioconductor
BiocManager::install(‘OmnipathR’, version = ‘3.12’)
```

--------------------------------

### preppi_download

Source: https://r.omnipathdb.org/reference/index.html

Downloads interactions from PrePPI.

```APIDOC
## preppi_download()

### Description
Interactions from PrePPI.

### Method
N/A (Function Call)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Run NicheNet Main Function with Custom Parameters

Source: https://r.omnipathdb.org/reference/nichenet_main.html

Use this snippet to run the `nichenet_main` function, allowing for customization of network resources and optimization parameters. It demonstrates how to exclude specific resources, override default settings, and specify the number of CPU cores for optimization.

```R
if (FALSE) {
nichenet_results <- nichenet_main(
    # altering some network resource parameters, the rest
    # of the resources will be loaded according to the defaults
    signaling_network = list(
        cpdb = NULL, # this resource will be excluded
        inbiomap = NULL,
        evex = list(min_confidence = 1.0) # override some parameters
    ),
    gr_network = list(only_omnipath = TRUE),
    n_top_ligands = 20,
    # override the default number of CPU cores to use
    mlrmbo_optimization_param = list(ncores = 4)
)
}
```

--------------------------------

### Get summary of annotations database

Source: https://r.omnipathdb.org/reference/database_summary.html

Call `database_summary` with 'annotations' to get a summary of the annotations database. The result is a list by default.

```r
annotations_summary <- database_summary('annotations')
```

--------------------------------

### Build NicheNet GR Network with Defaults

Source: https://r.omnipathdb.org/reference/nichenet_gr_network.html

Loads all network resources with their default parameters. This is the most comprehensive network prior.

```r
gr_network <- nichenet_gr_network()
```

--------------------------------

### Example of using nichenet_gr_network_remap

Source: https://r.omnipathdb.org/reference/nichenet_gr_network_remap.html

Demonstrates how to use the `nichenet_gr_network_remap` function with a specific `top_targets` value to limit the number of targets per transcription factor.

```R
# use only max. top 100 targets for each TF:
remap_gr_network <- nichenet_gr_network_remap(top_targets = 100)
```

--------------------------------

### Get Log File Path for a Specific Package

Source: https://r.omnipathdb.org/reference/omnipath_logfile.html

Use the `logfile` function to get the log file path for a specified package. Defaults to 'OmnipathR'.

```R
logfile(pkg = "OmnipathR")
```

--------------------------------

### Create and retrieve a cache record

Source: https://r.omnipathdb.org/reference/omnipath_cache_get.html

Demonstrates creating an example cache record for a Bioconductor URL and then retrieving it using `omnipath_cache_get`. Includes cleanup of the created record.

```R
# create an example cache record
bioc_url <- 'https://bioconductor.org/'
version <- omnipath_cache_latest_or_new(url = bioc_url)
omnipath_cache_remove(url = bioc_url) # cleaning up

# retrieve the cache record
record <- omnipath_cache_get(url = bioc_url)
record$key
#> [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a"
# [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a"
record$url
#> [1] "https://bioconductor.org/"
# [1] "https://bioconductor.org/"
```

--------------------------------

### Get Complex Resources - R

Source: https://r.omnipathdb.org/reference/get_complex_resources.html

Call this function to get the names of complex databases available on Omnipath.org. The `dataset` argument is ignored for this query type.

```r
get_complex_resources()
```

--------------------------------

### Install Required R Packages

Source: https://r.omnipathdb.org/articles/paths.html

Checks for and installs missing R packages required for the OmnipathR package and its dependencies. Ensures all necessary libraries are available before proceeding.

```R
library(OmnipathR)
library(magrittr)
library(dplyr)
library(purrr)
library(igraph)
library(rlang)
library(tidyselect)
library(stringr)
library(rmarkdown)
```

```R
missing_packages <-
    setdiff(
        c(
            'magrittr',
            'dplyr',
            'purrr',
            'igraph',
            'rlang',
            'tidyselect',
            'stringr',
            'rmarkdown',
            'devtools'
        ),
        installed.packages()
    )

if(length(missing_packages)){
    install.packages(missing_packages)
}

if(!'OmnipathR' %in% installed.packages()){
    library(devtools)
    devtools::install_github('saezlab/OmnipathR')
}
```

--------------------------------

### Build NicheNet LR Network with Defaults

Source: https://r.omnipathdb.org/reference/nichenet_lr_network.html

Loads all network resources with default parameters. This is the most common usage.

```r
lr_network <- nichenet_lr_network()
```

--------------------------------

### Build human PKN for COSMOS

Source: https://r.omnipathdb.org/reference/cosmos_pkn.html

Downloads, processes, and combines resources to build the human prior knowledge network. This operation can take a significant amount of time, especially on the first run.

```R
if (FALSE) { # \dontrun{
    human_cosmos <- cosmos_pkn(organism = "human")
} # }
```

--------------------------------

### Download Ligand-Receptor Network from Guide to Pharmacology

Source: https://r.omnipathdb.org/reference/nichenet_lr_network_guide2pharma.html

Use this function to download ligand-receptor interactions from the Guide to Pharmacology database. The output is a data frame formatted for NicheNet.

```R
g2p_lr_network <- nichenet_lr_network_guide2pharma()
```

--------------------------------

### Load Package Configuration

Source: https://r.omnipathdb.org/reference/omnipath_load_config.html

Use `omnipath_load_config` or `load_config` to load package settings from a specified configuration file. The `path` argument specifies the file, `title` selects a specific configuration within the file, and `user` can force the use of user-level configurations.

```R
omnipath_load_config(path = NULL, title = "default", user = FALSE, ...)

load_config(
  path = NULL,
  title = "default",
  user = FALSE,
  pkg = "OmnipathR",
  ...
)
```

```R
omnipath_load_config(path = 'my_custom_omnipath_config.yml')
```

--------------------------------

### Run NicheNet Main with Small Network Option

Source: https://r.omnipathdb.org/articles/nichenet.html

Initiates the NicheNet analysis using a pre-defined small network option, which utilizes a high-quality subset of OmniPath data for faster testing.

```r
nichenet_results <- nichenet_main(small = TRUE)
```

--------------------------------

### zenodo_download

Source: https://r.omnipathdb.org/reference/index.html

Retrieves data from Zenodo.

```APIDOC
## zenodo_download()

### Description
Retrieves data from Zenodo.

### Method
N/A (Function Call)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Get Interaction Resources - R

Source: https://r.omnipathdb.org/reference/get_interaction_resources.html

Retrieves the names of available interaction databases from Omnipath.org. Call without arguments to get all available resources, or specify a dataset for a filtered list.

```r
get_interaction_resources(dataset = NULL)
```

--------------------------------

### Retrieve KEGG Database Entries

Source: https://r.omnipathdb.org/reference/kegg_api_templates.html

This example demonstrates how to retrieve information about database entries from KEGG. It shows the structure of the returned data, including available database types.

```r
ddi <- keggGet("drug")
ddi[[1]]
ddi[[1]]$dbentries

```

--------------------------------

### Custom stitch_network example

Source: https://r.omnipathdb.org/reference/stitch_network.html

An example of calling stitch_network with custom protein and metabolite ID types. This call specifically requests 'genesymbol' for proteins and 'hmdb' for metabolites, while using default values for other parameters.

```R
stn <- stitch_network(protein_ids = 'genesymbol', metabolite_ids = 'hmdb')
```

--------------------------------

### Build Complete COSMOS Protein-Kinase Network (PKN)

Source: https://r.omnipathdb.org/articles/cosmos.html

Constructs the complete COSMOS PKN by integrating various data resources. This function can take a significant amount of time but benefits from caching. Use this for the final network build.

```r
pkn <- cosmos_pkn()

pkn
```

--------------------------------

### Get OmnipathR Log File Path

Source: https://r.omnipathdb.org/reference/omnipath_logfile.html

Retrieves the path to the current log file for the OmnipathR package. It can also be used to get the log file path for other packages by specifying the `pkg` argument.

```APIDOC
## Function: omnipath_logfile

### Description
Path to the current OmnipathR log file.

### Usage
```R
omnipath_logfile()
logfile(pkg = "OmnipathR")
```

### Arguments
#### pkg
- **pkg** (Character) - Required - Name of a package.

### Value
Character: path to the current logfile, or `NULL` if no logfile is available.

### Examples
```R
omnipath_logfile()
#> [1] "/home/denes/omnipathr/omnipathr-log/omnipathr-20241201-2047.log"
# [1] "/home/denes/omnipathr/omnipathr-log/omnipathr-20210309-1642.log"
```
```

--------------------------------

### Load a built-in database

Source: https://r.omnipathdb.org/reference/load_db.html

Loads a specified built-in database. Use 'omnipath_show_db()' to see available databases and their keys. The loaded database is accessible via 'get_db()'.

```R
load_db('go_slim')
```

```R
omnipath_show_db()
#> # A tibble: 23 × 10
#>    name    last_used           lifetime package loader loader_param latest_param
#>    <chr>   <dttm>                 <dbl> <chr>   <chr>  <list>       <list>      
#>  1 Gene O… 2024-04-07 17:01:07      300 Omnipa… go_on… <named list> <named list>
#>  2 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  3 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  4 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  5 Gene O… 2024-04-07 17:05:01      300 Omnipa… go_on… <named list> <named list>
#>  6 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  7 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  8 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#>  9 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#> 10 Gene O… NA                       300 Omnipa… go_on… <named list> <lgl [1]>   
#> # ℹ 13 more rows
#> # ℹ 3 more variables: loaded <lgl>, db <list>, key <chr>
```

--------------------------------

### Run NicheNet Main with Quality Filtering Parameters

Source: https://r.omnipathdb.org/articles/nichenet.html

Executes the main NicheNet analysis pipeline, applying custom quality filtering parameters for the ligand-receptor network.

```r
nichenet_results <- nichenet_main(
    quality_filter_param = list(
        min_curation_effort = 1,
        consensus_percentile = 30
    )
)
```

--------------------------------

### Example: Clean UniProt Gene Symbols

Source: https://r.omnipathdb.org/reference/uniprot_genesymbol_cleanup.html

This example demonstrates how to use the `uniprot_genesymbol_cleanup` function with a specific TrEMBL ID and organism. The output is a data frame showing the input ID and its corresponding SwissProt ID if a mapping is found.

```r
if (FALSE) {
uniprot_genesymbol_cleanup('Q6PB82', organism = 10090)
# # A tibble: 1 × 2
#   input  output
#   <chr>  <chr>
# 1 Q6PB82 O70405
}
```

--------------------------------

### ramp_sqlite

Source: https://r.omnipathdb.org/reference/index.html

Downloads and opens the RaMP database as an SQLite file.

```APIDOC
## ramp_sqlite()

### Description
Download and open RaMP database SQLite.

### Method
N/A (Function Call)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Get UniProt ID type label

Source: https://r.omnipathdb.org/reference/uniprot_id_type.html

Use `uniprot_id_type` to get the UniProt specific ID type label for a given identifier type. This label can be used in UniProt REST queries. If the label cannot be translated, the input is returned unchanged.

```R
uniprot_id_type("entrez")
#> [1] "entrez"
# [1] "database(GeneID)"
```

--------------------------------

### Get Descendants of Ontology Terms

Source: https://r.omnipathdb.org/reference/descendants.html

Retrieves all descendant terms (children) of the specified ontology terms. Use `ids = FALSE` to get term names instead of IDs. The function can be slow on the first call due to database loading.

```R
descendants('GO:0005035', ids = FALSE)
#> [1] "tumor necrosis factor receptor activity"
#> [2] "death receptor activity"                
#> [3] "TRAIL receptor activity"                
#> [4] "TNFSF11 receptor activity"              
# [1] "tumor necrosis factor-activated receptor activity"
# [2] "TRAIL receptor activity"
# [3] "TNFSF11 receptor activity"
```

--------------------------------

### Run the NicheNet pipeline with a little dummy network

Source: https://r.omnipathdb.org/reference/nichenet_test.html

Loads a tiny network and runs the NicheNet pipeline with a low number of iterations for testing. This function is intended to ensure the computational integrity of the pipeline.

```APIDOC
## nichenet_test()

### Description
Loads a tiny network and runs the NicheNet pipeline with a low number of iterations in the optimization process. This way the pipeline runs in a reasonable time in order to test the code. Due to the random subsampling disconnected networks might be produced sometimes. If you see an error like "Error in if (sd(prediction_vector) == 0) ... missing value where TRUE/FALSE needed", the random subsampled input is not appropriate. In this case just interrupt and call again. This test ensures the computational integrity of the pipeline. If it fails during the optimization process, try to start it over several times, even restarting R. The unpredictability is related to `mlrMBO` and `nichenetr` not being prepared to handle certain conditions, and it's also difficult to find out which conditions lead to which errors. At least 3 different errors appear time to time, depending on the input. It also seems like restarting R sometimes helps, suggesting that the entire system might be somehow stateful. You can ignore the ` Parallelization was not stopped` warnings on repeated runs.

### Usage
```R
nichenet_test(...)
```

### Arguments

`...`

Passed to `nichenet_main`.

### Value
A named list with the intermediate and final outputs of the pipeline: `networks`, `expression`, `optimized_parameters`, `weighted_networks` and `ligand_target_matrix`.

### Examples

```R
if (FALSE) {
nnt <- nichenet_test()
}
```
```

--------------------------------

### Print Interactions with Default Settings

Source: https://r.omnipathdb.org/reference/print_interactions.html

Prints a subset of enzyme-substrate interactions. Includes a warning for uninitialized columns.

```R
enzsub <- enzyme_substrate()
print_interactions(head(enzsub))
```

--------------------------------

### Build NicheNet Signaling Network

Source: https://r.omnipathdb.org/reference/nichenet_signaling_network_vinayagam.html

Generates a signaling network data frame for NicheNet. The example shows a basic call to the function. Note that the example output indicates an HTTP 403 error, suggesting potential access issues with the supplementary data.

```R
vi_signaling_network <- nichenet_signaling_network_vinayagam()
#> Error in (function (url, post = NULL, ...) {    httr <- handle_name <- NULL    http_param %<>% ensure_list_2    content_param %<>% ensure_list_2    http_method <- if (is.null(post))         GET    else POST    http_param$body <- post    req_headers %<>% list_null    http_param %<>% c(list(add_headers(.headers = unlist(req_headers)),         handle = structure(list(handle = new_handle(CONNECTTIMEOUT = getOption("omnipath.connect_timeout")),             url = (httr %:::% handle_name)(url))), class = "handle")))    if (!is.null(path)) {        http_param %<>% c(list(write_disk(path, overwrite = TRUE)))    }    response <- exec(http_method, url = url, !!!http_param)    http_status <- status_code(response)    msg <- sprintf("HTTP %i", http_status)    if (http_status != 200L) {        log_warn(msg)        stop(msg)    }    log_trace(msg)    result <- FALSE    if (return_response) {        result <- response    }    else {        if (is.null(path) && !ignore_contents) {            result <- exec(content, response, !!!content_param) %>%                 reader(...)        }    }    if (keep_headers) {        attr(result, "headers") <- headers(response)    }    return(result)})("https://www.science.org/action/downloadSupplement?doi=10.1126%2Fscisignal.2001699&file=4_rs8_tables_s1_s2_and_s6.zip"): HTTP 403
```

--------------------------------

### walk_ontology_tree

Source: https://r.omnipathdb.org/reference/index.html

Retrieves all nodes of a subtree starting from the selected nodes.

```APIDOC
## walk_ontology_tree()

### Description
All nodes of a subtree starting from the selected nodes.

### Method
N/A (Function Call)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```
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