tsea_dup_hyperG {signatureSearch} | R Documentation

## Target Set Enrichment Analysis (TSEA) with Hypergeometric Test

### Description

The `tsea_dup_hyperG` function performs Target Set Enrichment Analysis (TSEA) based on a modified hypergeometric test that supports test sets with duplications. This is achieved by maintaining the frequency information of duplicated items in form of weighting values.

### Usage

    tsea_dup_hyperG(
      drugs,
      universe = "Default",
      type = "GO",
      ont = "MF",
      pAdjustMethod = "BH",
      pvalueCutoff = 0.05,
      qvalueCutoff = 0.05,
      minGSSize = 5,
      maxGSSize = 500,
      dt_anno = "all",
      readable = FALSE
    )

### Arguments

`drugs` | character vector containing drug identifiers used for functional enrichment testing. This can be the top ranking drugs from a GESS result. Internally, drug test sets are translated to the corresponding target protein test sets based on the drug-target annotations provided under the `dt_anno` argument.
`universe` | character vector defining the universe of genes/proteins. If set as 'Default', it uses all genes/proteins present in the corresponding annotation system (e.g. GO, KEGG or Reactome). If 'type' is 'GO', it can be assigned a custom vector of gene SYMBOL IDs. If 'type' is 'KEGG' or 'Reactome', the vector needs to contain Entrez gene IDs.
`type` | one of 'GO', 'KEGG' or 'Reactome'
`ont` | character(1). If type is 'GO', assign `ont` (ontology) one of 'BP','MF', 'CC' or 'ALL'. If type is 'KEGG' or 'Reactome', `ont` is ignored.
`pAdjustMethod` | p-value adjustment method, one of 'holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr'
`pvalueCutoff` | double, p-value cutoff
`qvalueCutoff` | double, qvalue cutoff
`minGSSize` | integer, minimum size of each gene set in annotation system
`maxGSSize` | integer, maximum size of each gene set in annotation system
`dt_anno` | drug-target annotation source. It is the same argument as the `database` argument of the `get_targets` function. Usually, it is recommended to set the 'dt_anno' to 'all' since it provides the most complete drug-target annotations. Choosing a single annotation source results in sparser drug-target annotations (particularly CLUE), and thus less complete enrichment results.
`readable` | TRUE or FALSE, it applies when type is 'KEGG' or 'Reactome' indicating whether to convert gene Entrez ids to gene Symbols in the 'itemID' column in the result table.

### Details

The classical hypergeometric test assumes uniqueness in its test sets. To maintain the duplication information in the test sets used for TSEA, the values of the total number of genes/proteins in the test set and the number of genes/proteins in the test set annotated at a functional category are adjusted by maintaining their frequency information in the test set rather than counting each entry only once. Removing duplications in TSEA would be inappropriate since it would erase one of the most important pieces of information of this approach.

### Value

`feaResult` object, the result table contains the enriched functional categories (e.g. GO terms or KEGG pathways) ranked by the corresponding enrichment statistic.

### Column description

The TSEA results (including `tsea_dup_hyperG`) stored in the `feaResult` object can be returned with the `result` method in tabular format, here `tibble`. The columns of this `tibble` are described below.

- GeneRatio: ratio of genes in the test set that are annotated at a specific GO node or KEGG pathway

- BgRatio: ratio of background genes that are annotated at a specific GO node or KEGG pathway

- pvalue: raw p-value of enrichment test

Additional columns are described under the 'result' slot of the `feaResult` object.

### See Also

`feaResult`, `fea`

### Examples

    data(drugs10)
    ## GO annotation system
    # res1 <- tsea_dup_hyperG(drugs=drugs10, universe="Default",
    #                         type="GO", ont="MF", pvalueCutoff=0.05,
    #                         pAdjustMethod="BH", qvalueCutoff=0.1,
    #                         minGSSize=5, maxGSSize=500)
    # result(res1)
    #
    ## KEGG annotation system
    # res2 <- tsea_dup_hyperG(drugs=drugs10, type="KEGG",
    #                         pvalueCutoff=0.1, qvalueCutoff=0.2,
    #                         minGSSize=10, maxGSSize=500)
    #
    ## Reactome annotation system
    # res3 <- tsea_dup_hyperG(drugs=drugs10, type="Reactome",
    #                         pvalueCutoff=1, qvalueCutoff=1)

[Package signatureSearch version 1.8.2 Index]