singlelinc-methods: Co-Expression Analysis Of A Single ncRNA Gene
In LINC: co-expression of lincRNAs and protein-coding genes

Description Usage Arguments Details Value Methods Compatibility Author(s) See Also Examples

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.

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

`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

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 ofclusterProfiler.

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

signature(input = "LINCmatrix"): (see details)

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

Manuel Goepferich

linc ; singlelinc

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)