singlelinc-methods {LINC} | R Documentation

## Co-Expression Analysis Of A Single ncRNA Gene

### Description

The function `singlelinc` perfroms co-expression analysis for a single query. An input `LINCmatrix` will be converted to a `LINCsingle` object. As a first step (I) a set of co-expressed protein-coding genes of a query is determined. Secondly, (II) biological terms related to the these genes are derived. The result will show the co-expression for the query.

### Usage

    singlelinc(input,
               query = NULL,
            onlycor = FALSE,
            testFun = cor.test,
        alternative = "greater",
          threshold = 0.05,
            underth = FALSE,
          coExprCut = NULL,
         enrichFun  = 'enrichGO',
               ont  = "BP",
            verbose = TRUE,
            ...)

### Arguments

`input` | an object of the class `LINCmatrix`
`query` | the name of the (ncRNA) gene to be evaluated; has to be present in `input`
`onlycor` | if `TRUE` co-expression will be decided based on absolute correlation values from the input `LINCmatrix` object. If `FALSE` co-expressed genes will be selected based on p-values from the correlation test.
`testFun` | a function to test the robustness of correlations. User-defined functions are allowed. The expected output is a p-value.
`alternative` | one of `c("two.sided", "less", "greater")`. This argument indicates the alternative in the correlation test. `"less"` instead of `"greater"` can be used for negative correlations.
`threshold` | a single number representing the threshold for selecting co-expressed genes
`underth` | if `TRUE` values lower than the threshold will be considered (intended for p-values). If `FALSE` values higher tahn the threshold will be considered (intended for absolute correlations)
`coExprCut` | a single `integer` indicating the maximal number of co-expressed genes to select. In case too many genes fulfill the supplied `threshold` criterion their number can be reduced by this argument.
`enrichFun` | a function given as character string which will derive significant biological terms based on the set of co-expressed genes from a gene annotation resource. Supported functions are: `c("enrichGO", "enrichPathway", "enrichDO")`
`ont` | a subontology, only used for `enrichFun = 'enrichGO'`. This has to be one of `"MF", "BP", "CC"`.
`verbose` | whether to give messages about the progression of the function `TRUE` or not `FALSE`
`...` | further arguments, mainly for `cor.test` and functions from clusterProfiler

### Details

In comparison to the function `clusterlinc` this function will provide more flexibility in terms of the selection of co-expressed genes. The option `onlycor = TRUE` in combination with a suitable `threshold` can be used to choose co-expressed protein-coding genes based on the correlation values inherited from the input `LINCmatrix` object. For this to work it is required to set `underth = FALSE` because then, values higher than the `threshold` will be picked. By default, co-expression depnds on the p-values from the correlation test (`stats::cor.test`) which demonstrate the robustness of a given correlation between two genes. A user-defined test function supplied in `testFun` requires the formal arguments `x`, `y`, `method` and `use`. Moreover, the p-values of the output should be accessible by `$pvalue`. The number of co-expressed genes can be restricted not only by `threshold`, but also by `coExprCut`. The value `n` for `coExprCut = n` will be ignored in case the number of genes which fulfill the `threshold` criterion is smaller than `n`. Options for `enrichFun` are for example: `ReactomePA::enrichPathway()` or `clusterProfiler::enrichGO`. Further arguments (`...`) are inteded to be passed to the called `enrichFun` function. `enrichFun = 'enrichGO', ont = "CC"` will call the subontology "Cellular Component" from GO. In case genes are not given as Entrez ids they will be translated. For more details see the documentation of`clusterProfiler`.

### Value

an object of the class 'LINCmatrix' (S4) with 6 `Slots`

`results` | a `list` of four entries: `$query`, the queried gene, `$bio`, a list of biological terms and their p-values, `$cor`, absolute correlations of co-expressed genes, `$pval`, p-values from the correlation test of co-expressed genes
`assignment` | a `character` vector of protein-coding genes
`correlation` | a `list` of `$single`, the correlation of the query to protein-coding genes
`expression` | the original expression matrix
`history` | a storage environment of important methods, objects and parameters used to create the object
`linCenvir` | a storage environment ensuring the compatibility to other objects of the `LINC` class

### Methods

`signature(input = "LINCmatrix")`

  (see details)

### Compatibility

`plotlinc(LINCsingle, ...))`, ...

### Author(s)

Manuel Goepferich

### See Also

`linc` ; `singlelinc`

### Examples

    data(BRAIN_EXPR)

    # selection based on absolute correlation
    meg3 <- singlelinc(crbl_matrix, query = "55384", onlycor = TRUE, underth = FALSE, threshold = 0.5)
    plotlinc(meg3)

    # using the 'cor.test' in combination with 'underth = TRUE'
    meg3 <- singlelinc(crbl_matrix, query = "55384", underth = TRUE, threshold = 0.0005, ont = 'BP')
    plotlinc(meg3)

[Package LINC version 1.8.0 Index]