R: WebGestaltR: The R interface for enrichment analysis with...

WebGestaltR {WebGestaltR} | R Documentation

## WebGestaltR: The R interface for enrichment analysis with WebGestalt.

### Description

Main function for enrichment analysis

### Usage

    WebGestaltR(
      enrichMethod = "ORA",
      organism = "hsapiens",
      enrichDatabase = NULL,
      enrichDatabaseFile = NULL,
      enrichDatabaseType = NULL,
      enrichDatabaseDescriptionFile = NULL,
      interestGeneFile = NULL,
      interestGene = NULL,
      interestGeneType = NULL,
      collapseMethod = "mean",
      referenceGeneFile = NULL,
      referenceGene = NULL,
      referenceGeneType = NULL,
      referenceSet = NULL,
      minNum = 10,
      maxNum = 500,
      sigMethod = "fdr",
      fdrMethod = "BH",
      fdrThr = 0.05,
      topThr = 10,
      reportNum = 20,
      perNum = 1000,
      gseaP = 1,
      isOutput = TRUE,
      outputDirectory = getwd(),
      projectName = NULL,
      dagColor = "continuous",
      saveRawGseaResult = FALSE,
      gseaPlotFormat = c("png", "svg"),
      setCoverNum = 10,
      networkConstructionMethod = NULL,
      neighborNum = 10,
      highlightType = "Seeds",
      highlightSeedNum = 10,
      nThreads = 1,
      cache = NULL,
      hostName = "http://www.webgestalt.org/",
      ...
    )

    WebGestaltRBatch(
      interestGeneFolder = NULL,
      enrichMethod = "ORA",
      isParallel = FALSE,
      nThreads = 3,
      ...
    )

### Arguments

`enrichMethod` | Enrichment methods: `ORA`, `GSEA` or `NTA`.
`organism` | Currently, WebGestaltR supports 12 organisms. Users can use the function `listOrganism` to check available organisms. Users can also input `others` to perform the enrichment analysis for other organisms not supported by WebGestaltR. For other organisms, users need to provide the functional categories, interesting list and reference list (for ORA method). Because WebGestaltR does not perform the ID mapping for the other organisms, the above data should have the same ID type.
`enrichDatabase` | The functional categories for the enrichment analysis. Users can use the function `listGeneSet` to check the available functional databases for the selected organism. Multiple databases in a vector are supported for ORA and GSEA.
`enrichDatabaseFile` | Users can provide one or more GMT files as the functional category for enrichment analysis. The extension of the file should be `gmt` and the first column of the file is the category ID, the second one is the external link for the category. Genes annotated to the category are from the third column. All columns are separated by tabs. The GMT files will be combined with `enrichDatabase`.
`enrichDatabaseType` | The ID type of the genes in the `enrichDatabaseFile`. If users set `organism` as `others`, users do not need to set this ID type because WebGestaltR will not perform ID mapping for other organisms. The supported ID types of WebGestaltR for the selected organism can be found by the function `listIdType`.
`enrichDatabaseDescriptionFile` | Users can also provide description files for the custom `enrichDatabaseFile`. The extension of the description file should be `des`. The description file contains two columns: the first column is the category ID that should be exactly the same as the category ID in the custom `enrichDatabaseFile` and the second column is the description of the category. All columns are separated by tabs.
`interestGeneFile` | If `enrichMethod` is `ORA` or `NTA`, the extension of the `interestGeneFile` should be `txt` and the file can only contain one column: the interesting gene list. If `enrichMethod` is `GSEA`, the extension of the `interestGeneFile` should be `rnk` and the file should contain two columns separated by tab: the gene list and the corresponding scores.
`interestGene` | Users can also use an R object as the input. If `enrichMethod` is `ORA` or `NTA`, `interestGene` should be an R `vector` object containing the interesting gene list. If `enrichMethod` is `GSEA`, `interestGene` should be an R `data.frame` object containing two columns: the gene list and the corresponding scores.
`interestGeneType` | The ID type of the interesting gene list. The supported ID types of WebGestaltR for the selected organism can be found by the function `listIdType`. If the `organism` is `others`, users do not need to set this parameter.
`collapseMethod` | The method to collapse duplicate IDs with scores. `mean`, `median`, `min` and `max` represent the mean, median, minimum and maximum of scores for the duplicate IDs.
`referenceGeneFile` | For the ORA method, the users need to upload the reference gene list. The extension of the `referenceGeneFile` should be `txt` and the file can only contain one column: the reference gene list.
`referenceGene` | For the ORA method, users can also use an R object as the reference gene list. `referenceGene` should be an R `vector` object containing the reference gene list.
`referenceGeneType` | The ID type of the reference gene list. The supported ID types of WebGestaltR for the selected organism can be found by the function `listIdType`. If the `organism` is `others`, users do not need to set this parameter.
`referenceSet` | Users can directly select the reference set from existing platforms in WebGestaltR and do not need to provide the reference set through `referenceGeneFile`. All existing platforms supported in WebGestaltR can be found by the function `listReferenceSet`. If `referenceGeneFile` and `refereneceGene` are `NULL`, WebGestaltR will use the `referenceSet` as the reference gene set. Otherwise, WebGestaltR will use the user supplied reference set for enrichment analysis.
`minNum` | WebGestaltR will exclude the categories with the number of annotated genes less than `minNum` for enrichment analysis. The default is `10`.
`maxNum` | WebGestaltR will exclude the categories with the number of annotated genes larger than `maxNum` for enrichment analysis. The default is `500`.
`sigMethod` | Two methods of significance are available in WebGestaltR: `fdr` and `top`. `fdr` means the enriched categories are identified based on the FDR and `top` means all categories are ranked based on FDR and then select top categories as the enriched categories. The default is `fdr`.
`fdrMethod` | For the ORA method, WebGestaltR supports five FDR methods: `holm`, `hochberg`, `hommel`, `bonferroni`, `BH` and `BY`. The default is `BH`.
`fdrThr` | The significant threshold for the `fdr` method. The default is `0.05`.
`topThr` | The threshold for the `top` method. The default is `10`.
`reportNum` | The number of enriched categories visualized in the final report. The default is `20`. A larger `reportNum` may be slow to render in the report.
`perNum` | The number of permutations for the GSEA method. The default is `1000`.
`gseaP` | The exponential scaling factor of the phenotype score. The default is `1`. When p=0, ES reduces to standard K-S statistics (See original paper for more details).
`isOutput` | If `isOutput` is TRUE, WebGestaltR will create a folder named by the `projectName` and save the results in the folder. Otherwise, WebGestaltR will only return an R `data.frame` object containing the enrichment results. If hundreds of gene list need to be analyzed simultaneously, it is better to set `isOutput` to `FALSE`. The default is `TRUE`.
`outputDirectory` | The output directory for the results.
`projectName` | The name of the project. If `projectName` is `NULL`, WebGestaltR will use time stamp as the project name.
`dagColor` | If `dagColor` is `binary`, the significant terms in the DAG structure will be colored by steel blue for ORA method or steel blue (positive related) and dark orange (negative related) for GSEA method. If `dagColor` is `continous`, the significant terms in the DAG structure will be colored by the color gradient based on corresponding FDRs.
`saveRawGseaResult` | Whether the raw result from GSEA is saved as a RDS file, which can be used for plotting. Defaults to `FALSE`. The list includes Enrichment_Results A data frame of GSEA results with statistics Running_Sums A matrix of running sum of scores for each gene set Items_in_Set A list with ranks of genes for each gene set
`gseaPlotFormat` | The graphic format of GSEA enrichment plots. Either `svg`, `png`, or `c("png", "svg")` (default).
`setCoverNum` | The number of expected gene sets after set cover to reduce redundancy. It could get fewer sets if the coverage reaches 100%. The default is `10`.
`networkConstructionMethod` | Netowrk construction method for NTA. Either `Network_Retrieval_Prioritization` or `Network_Expansion`. Network Retrieval & Prioritization first uses random walk analysis to calculate random walk probabilities for the input seeds, then identifies the relationships among the seeds in the selected network and returns a retrieval sub-network. The seeds with the top random walk probabilities are highlighted in the sub-network. Network Expansion first uses random walk analysis to rank all genes in the selected network based on their network proximity to the input seeds and then return an expanded sub-network in which nodes are the input seeds and their top ranking neighbors and edges represent their relationships.
`neighborNum` | The number of neighbors to include in NTA Network Expansion method.
`highlightType` | The type of nodes to highlight in the NTA Network Expansion method, either `Seeds` or `Neighbors`.
`highlightSeedNum` | The number of top input seeds to highlight in NTA Network Retrieval & Prioritizaiton method.
`nThreads` | The number of cores to use for GSEA and set cover, and in batch function.
`cache` | A directory to save data cache for reuse. Defaults to `NULL` and disabled.
`hostName` | The server URL for accessing data. Mostly for development purposes.
`...` | In batch function, passes parameters to WebGestaltR function. Also handles backward compatibility for some parameters in old versions.
`interestGeneFolder` | Run WebGestaltR for gene list files in the folder.
`isParallel` | If jobs are run parallelly in the batch.

### Details

WebGestaltR function can perform three enrichment analyses: ORA (Over-Representation Analysis) and GSEA (Gene Set Enrichment Analysis).and NTA (Network Topology Analysis). Based on the user-uploaded gene list or gene list with scores, WebGestaltR function will first map the gene list to the entrez gene ids and then summarize the gene list based on the GO (Gene Ontology) Slim. After performing the enrichment analysis, WebGestaltR function also returns a user-friendly HTML report containing GO Slim summary and the enrichment analysis result. If functional categories have DAG (directed acyclic graph) structure or genes in the functional categories have network structure, those relationship can also be visualized in the report.

### Value

The WebGestaltR function returns a data frame containing the enrichment analysis result and also outputs an user-friendly HTML report if `isOutput` is `TRUE`. The columns in the data frame depend on the `enrichMethod` and they are the following:

geneSet

  ID of the gene set.

description

  Description of the gene set if available.

link

  Link to the data source.

size

  The number of genes in the set after filtering by `minNum` and `maxNum`.

overlap

  The number of mapped input genes that are annotated in the gene set.

expect

  Expected number of input genes that are annotated in the gene set.

enrichmentRatio

  Enrichment ratio, overlap / expect.

enrichmentScore

  Enrichment score, the maximum running sum of scores for the ranked list.

normalizedEnrichmentScore

  Normalized enrichment score, normalized against the average enrichment score of all permutations.

leadingEdgeNum

  Number of genes/phosphosites in the leading edge.

pValue

  P-value from hypergeometric test for ORA. For GSEA, please refer to its original publication or online at https://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm.

FDR

  Corrected P-value for mulilple testing with `fdrMethod` for ORA.

overlapId

  The gene/phosphosite IDs of `overlap` for ORA (entrez gene IDs or phosphosite sequence).

leadingEdgeId

  Genes/phosphosites in the leading edge in entrez gene ID or phosphosite sequence.

userId

  The gene/phosphosite IDs of `overlap` for ORA or `leadingEdgeId` for GSEA in User input IDs.

plotPath

  Path of the GSEA enrichment plot.

database

  Name of the source database if multiple enrichment databases are given.

goId

  In NTA, like `geneSet`, the enriched GO terms of genes in the returned subnetwork.

interestGene

  In NTA, the gene IDs in the subnetwork with 0/1 annotations indicating if it is from user input.

The WebGestaltRBatch function returns a list of enrichment results.

### Examples

    ## Not run:
    ####### ORA example #########
    geneFile <- system.file("extdata", "interestingGenes.txt", package="WebGestaltR")
    refFile <- system.file("extdata", "referenceGenes.txt", package="WebGestaltR")
    outputDirectory <- getwd()
    enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
      enrichDatabase="pathway_KEGG", interestGeneFile=geneFile,
      interestGeneType="genesymbol", referenceGeneFile=refFile,
      referenceGeneType="genesymbol", isOutput=TRUE,
      outputDirectory=outputDirectory, projectName=NULL)

    ####### GSEA example #########
    rankFile <- system.file("extdata", "GeneRankList.rnk", package="WebGestaltR")
    outputDirectory <- getwd()
    enrichResult <- WebGestaltR(enrichMethod="GSEA", organism="hsapiens",
      enrichDatabase="pathway_KEGG", interestGeneFile=rankFile,
      interestGeneType="genesymbol", sigMethod="top", topThr=10, minNum=5,
      outputDirectory=outputDirectory)

    ####### NTA example #########
    enrichResult <- WebGestaltR(enrichMethod="NTA", organism="hsapiens",
      enrichDatabase="network_PPI_BIOGRID", interestGeneFile=geneFile,
      interestGeneType="genesymbol", sigMethod="top", topThr=10,
      outputDirectory=getwd(), highlightSeedNum=10,
      networkConstructionMethod="Network_Retrieval_Prioritization")

    ## End(Not run)

[Package WebGestaltR version 0.4.3 Index]