crossomics {omicRexposome} | R Documentation

## Function to perform a Transcriptome-Wide Association Study

### Description

This function allows to perform a Transcriptome-Wide Association Study by using an `ExposmeSet` and an `ExpressionSet`. It allows to perform an adjustment using Surrogate Variable Analysis (from R package `sva`).

### Usage

    crossomics(object, method = "mcca", ncomponents = 2, ..., na.rm = FALSE,
      permute = c(100, 3), verbose = FALSE, warnings = TRUE)

    ## S4 method for signature 'MultiDataSet'
    crossomics(object, method = "mcca",
      ncomponents = 2, ..., na.rm = FALSE, permute = c(100, 3),
      verbose = FALSE, warnings = TRUE)

### Arguments

`object` | A `MultiDataSet` object containing at last two data-sets like `ExposomeSet`, `ExpressionSet`, `MethylationSet`...
`method` | (default `"mcca"`) It can takes values `"mcca"` for Multiple Canonical Correlation Analysis or `"mcia"` for Multiple Co-Inertia Analysis.
`ncomponents` | (default `2`) Number of components to be estimated.
`...` | Other arguments given to `mcia` (from `omicade4`) or to `MultiCCA` (from `PMA`).
`na.rm` | (default `FALSE`) If `method` was set to `"mcca"` and `na.rm` was set to `TRUE`, features containing missing values are removed.
`permute` | (default `c(100, 3)`). If `method="mcca"` and this agument is set to `NULL` no permutation test to tune-up the parameters for `MultiCCA`. When filles, `permute[1]` corresponds to the number permutations (default in `MultiCCa.permute` is `25`) and `permute[2]` the number of iterations (default in `MultiCCA.permute` is 3).
`verbose` | (default `FALSE`) If set to `TRUE`, a series of messages descriving the process are shown.
`warnings` | (default `TRUE`) If set to `TRUE`, a series of warnings are shown when required user atention.

### Value

An object of class `ResultSet`.

### Examples

    library(MultiDataSet)
    library(rexposome)
    data(brge_prot, package = "brgedata")
    data(brge_expo, package = "brgedata")
    mds <- createMultiDataSet()
    mds <- add_eset(mds, brge_prot, dataset.type = "proteines")
    mds <- add_eset(mds, imputation(brge_expo),
        dataset.type = "exposures", GRanges = NA)

    crs <- crossomics(mds, method = "mcia")
    crs

[Package omicRexposome version 1.9.0 Index]