gess_fisher {signatureSearch} | R Documentation

## Fisher Search Method

### Description

In its iterative form, Fisher's exact test (Upton, 1992) can be used as Gene Expression Signature (GES) Search to scan GES databases for entries that are similar to a query GES.

### Usage

    gess_fisher(qSig, higher, lower, chunk_size = 5000, ref_trts = NULL)

### Arguments

`qSig` | `qSig` object defining the query signature including the GESS method (should be 'Fisher') and the path to the reference database. For details see help of `qSig` and `qSig-class`.
`higher` | The 'higher' threshold. If not 'NULL', genes with a score larger than 'higher' will be included in the gene set with sign +1. At least one of 'lower' and 'higher' must be specified.
`lower` | The lower threshold. If not 'NULL', genes with a score smaller than 'lower' will be included in the gene set with sign -1. At least one of 'lower' and 'higher' must be specified.
`chunk_size` | number of database entries to process per iteration to limit memory usage of search.
`ref_trts` | character vector. If users want to search against a subset of the reference database, they could set ref_trts as a character vector representing column names (treatments) of the subsetted refdb.

### Details

When using the Fisher's exact test (Upton, 1992) as GES Search (GESS) method, both the query and the database are composed of gene label sets, such as DEG sets.

### Value

`gessResult` object, the result table contains the search results for each perturbagen in the reference database ranked by their signature similarity to the query.

### Column description

Descriptions of the columns specific to the Fisher method are given below. Note, the additional columns, those that are common among the GESS methods, are described in the help file of the `gessResult` object.

- pval: p-value of the Fisher's exact test.

- padj: p-value adjusted for multiple hypothesis testing using R's p.adjust function with the Benjamini & Hochberg (BH) method.

- effect: z-score based on the standard normal distribution.

- LOR: Log Odds Ratio.

- nSet: number of genes in the GES in the reference database (gene sets) after setting the higher and lower cutoff.

- nFound: number of genes in the GESs of the reference database (gene sets) that are also present in the query GES.

- signed: whether gene sets in the reference database have signs, representing up and down regulated genes when computing scores.

### References

Graham J. G. Upton. 1992. Fisher's Exact Test. J. R. Stat. Soc. Ser. A Stat. Soc. 155 (3). [Wiley, Royal Statistical Society]: 395-402. URL: http://www.jstor.org/stable/2982890

### See Also

`qSig`, `gessResult`, `gess`

### Examples

    db_path <- system.file("extdata", "sample_db.h5",
                           package = "signatureSearch")
    # library(SummarizedExperiment); library(HDF5Array)
    # sample_db <- SummarizedExperiment(HDF5Array(db_path, name="assay"))
    # rownames(sample_db) <- HDF5Array(db_path, name="rownames")
    # colnames(sample_db) <- HDF5Array(db_path, name="colnames")
    ## get "vorinostat__SKB__trt_cp" signature drawn from sample databass
    # query_mat <- as.matrix(assay(sample_db[,"vorinostat__SKB__trt_cp"]))
    # qsig_fisher <- qSig(query=query_mat, gess_method="Fisher", refdb=db_path)
    # fisher <- gess_fisher(qSig=qsig_fisher, higher=1, lower=-1)
    # result(fisher)

[Package signatureSearch version 1.0.4 Index]