clusterlinc-methods {LINC} | R Documentation

## Cluster Queried ncRNAs Based On Their Interaction Partners

### Description

The function `clusterlinc` will give an overview of ncRNAs in a dataset. An input `LINCmatrix` will be converted to a `LINCcluster`. The following steps are conducted (I) computation of a correlation test, (II) setup of a distance matrix, (III) calculation of a dendrogram and (IV) selection of co-expressed genes for each query. The result is a cluster of ncRNAs and their associated protein-coding genes.

### Usage

    clusterlinc(linc,
                distMethod    = "dicedist",
                clustMethod   = "average",
                pvalCutOff    = 0.05,
                padjust       = "none",
                coExprCut     = NULL,
                cddCutOff     = 0.05,
                verbose       = TRUE)

### Arguments

`linc` | an object of the class `LINCmatrix`
`distMethod` | a method to compute the distance between ncRNAs; has to be one of `c("correlation", "pvalue", "dicedist")`
`clustMethod` | an algorithm to compute the dendrogram, has to be one of `c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")`
`pvalCutOff` | a threshold for the selection of co-expressed genes. Only protein-coding genes showing a significance in the correlation test lower than `pvalCutOff` will assigned to queried ncRNAs as interaction partner.
`padjust` | one of `c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")`
`coExprCut` | a single `integer` indicating the number of co-expressed genes to select. If this argument is used for each ncRNA the `coExprCut = n` protein-coding genes with the lowest p-value in the correlation test will be assigned to queries, respectively.
`cddCutOff` | a threshold that is only relevant for `distMethod = "dicedist"`. In this method `cddCutOff` defines whether a ncRNA and a protein-coding gene can be considered as interaction partners. This influences the distance matrix and the clustering process.
`verbose` | whether to give messages about the progression of the function `TRUE` or not `FALSE`

### Details

As a first step `clusterlinc` conducts the correlation test (`stats::cor.test`) using the correlation method and handeling of missing values inherited from the input `LINCmatrix`. Resulting p-values indicate the statistical robustness of correlations instead of absolute correlation values. Co-expression of ncRNAs to protein-coding genes is assumed if the p-value from the `cor.test` is lower than the given `pvalCutOff`. An alternative way to select co-expressed genes is provided by `coExprCut`. This argument has priority over `pvalCutOff` and can be used to pick the `n` genes with the lowest p-value for each ncRNA. In contrast to `pvalCutOff`, this will result in an equal number of assigned co-expressed genes. The argument `padjust` can be used for multiple testing correction. In most cases this is not compatible with `distMethod = "dicedist"`.

For the computation of the distance matrix of ncRNA genes three methods can be applied. The first method `"correlation"` uses `1 - correlation` as distance measure. In contrast, `"pvalue"` considers not the absolute correlation values, but p-values from the correlation test. A third method is termed `"dicedist"` and takes the Czekanovski dice distance [1] as distance measure. Here, the number of shared interaction partners between ncRNAs determines their relation to each other. The argument `cddCutOff` is an option to decide which p-values in the correlation matrix can be considered as interaction. A low threshold, for instance, will consider only interactions of ncRNAs and protein-coding genes supported by a p-value lower than the supplied threshold and therfore a robust correlation of these two genes. Based on the distance matrix a cluster of the ncRNAs will be computed by `stats::hclust`. Argument `clustMethod` defines which clustering method should be applied.

A `LINCcluster` can be recalculated with the command `clusterlinc(LINCcluster, ...))` in order to change further arguments. `plotlinc(LINCcluster, ...))` will plot a figure that shows the cluster of ncRNAs (dendrogram) and the number of co-expressed genes with respect to different thresholds. `getbio(LINCcluster, ...))` will derive the biological terms associated with the co-expressed genes. Due to the correlation test longer calculation times can occur. A faster alternative to this function is `singlelinc()`. User-defined correlation test functions are supported for `singlelinc()` but not for `clusterlinc()`.

### Value

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

`results` | a `list` containing an object of the class `"phylo"` with the additional entry `neighbours`, a list of queries and co-expressed genes
`assignment` | a `character` vector of protein-coding genes
`correlation` | a `list` of `cormatrix`, the correlation of non-coding to protein-coding genes, `lnctolnc`, the correlation of non-coding to non-coding genes and `cortest`, p-values of the correlation test of non-coding 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(linc = "LINCcluster")`

  (see details)

`signature(linc = "LINCmatrix")`

  (see details)

### Compatibility

`plotlinc(LINCcluster, ...)`, `getbio(LINCcluster, ...)`, ...

### Author(s)

Manuel Goepferich

### References

[1] Christine Brun, Francois Chevenet, David Martin, Jerome Wojcik, Alain Guenoche and Bernard Jacq" Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network" (2003) Genome Biology, 5:R6.

### See Also

`linc` ; `singlelinc`

### Examples

    data(BRAIN_EXPR)
    class(crbl_matrix)

    # call 'clusterlinc' with no further arguments
    crbl_cluster <- clusterlinc(crbl_matrix)

    # apply the distance method "correlation instead of "dicedist"
    crbl_cluster_cor <- clusterlinc(crbl_matrix, distMethod = "correlation" )
    # do the same as recursive call using the 'LINCcluster' object
    # crbl_cluster_cor <- clusterlinc(crbl_cluster, distMethod = "correlation")

    # select 25 genes with lowest p-values for each query
    crbl_cluster_25 <- clusterlinc(crbl_matrix, coExprCut = 25)

    # select onyl those with a p-value < 5e-5
    crbl_cluster_5e5 <- clusterlinc(crbl_matrix, pvalCutOff = 5e-5)

    # adjust for multiple testing
    crbl_cluster_hochberg <- clusterlinc(crbl_matrix, distMethod = "correlation",
                                       padjust = "hochberg", pvalCutOff = 0.05)

    # comparing two distance methods
     plotlinc(crbl_cluster)
     plotlinc(crbl_cluster_cor)

[Package LINC version 1.8.0 Index]