dcPipeline {dcanr} | R Documentation

## Run a DC pipeline on a simulation

### Description

Run a differential co-expression pipeline on data from a simulation experiment. A default pipeline can be used which consists of methods in the package or custom pipelines can be provided.

### Usage

    dcPipeline(simulation, dc.func = "zscore", precomputed = FALSE,
      continuous = FALSE, cond.args = list(), ...)

### Arguments

`simulation` | a list, storing data and results generated from simulations
`dc.func` | a function or character. Character represents one of the method names from `dcMethods` which is run with the default settings. A function can be used to provide custom processing pipelines (see details)
`precomputed` | a logical, indicating whether the precomputed inference should be used or a new one computed (default FALSE)
`continuous` | a logical, indicating whether binary or continuous conditions should be used (default FALSE). No methods implemented currently use continuous conditions. This is to allow custom methods that require continuous conditions
`cond.args` | a list, containing condition-specific arguments for the DC inference pipeline. See details
`...` | additional parameters to `dc.func`

### Details

If `dc.func` is a character, the existing methods in the package will be run with their default parameters. The pipeline is as such: dcScore -> dcTest -> dcAdjust -> dcNetwork, resulting in a igraph object. Parameters to the independent processing steps can also be provided to this function as shown in the examples.

If `precomputed` is TRUE while `dc.func` is a character, pre-computed results will be used. These can then be evaluated using `dcEvaluate`.

Custom pipelines need to be coded into a function which can then be provided instead of a character. Functions must have the following structure:

`function(emat, condition, ...)`

They must return either an igraph object or an adjacency matrix stored in a base R 'matrix' or the S4 'Matrix' class, containing all genes in the expression matrix 'emat'. See examples for how the in-built functions are combined into a pipeline.

If the pipeline (in-built or custom) requires condition-specific parameters to run, cond.args can be used to pass these. For instance, LDGM requires lambda OR the number of edges in the target network to be specified for each inference/condition. For the latter case and with 3 different conditions, this can be done by setting `cond.args = list('ldgm.ntarget' = c(100, 140, 200))`. Non-specific arguments should be passed directly to the `dcPipeline` function call.

### Value

a list of igraphs, representing the differential network for each independent condition (knock-out).

### See Also

`plot.igraph`, `dcScore`, `dcTest`, `dcAdjust`, `dcNetwork`, `dcMethods`

### Examples

    data(sim102)

    #run a standard pipeline
    resStd <- dcPipeline(sim102, dc.func = 'zscore')

    #run a standard pipeline and specify params
    resParam <- dcPipeline(sim102, dc.func = 'zscore', cor.method = 'pearson')

    #run a standard pipeline and specify condition-specific params
    resParam <- dcPipeline(
      sim102,
      dc.func = 'diffcoex',
      #arguments for the conditions ADR1 knockdown and UME6 knockdown resp.
      cond.args = list(diffcoex.beta = c(6, 20))
    )

    #retrieve pre-computed results
    resPrecomputed <- dcPipeline(sim102, dc.func = 'zscore', precomputed = TRUE)

    #run a custom pipeline
    analysisInbuilt <- function(emat, condition, dc.method = 'zscore', ...) {
      #compute scores
      score = dcScore(emat, condition, dc.method, ...)
      #perform statistical test
      pvals = dcTest(score, emat, condition, ...)
      #adjust tests for multiple testing
      adjp = dcAdjust(pvals, ...)
      #threshold and generate network
      dcnet = dcNetwork(score, adjp, ...)

      return(dcnet)
    }
    resCustom <- dcPipeline(sim102, dc.func = analysisInbuilt)

    plot(resCustom[[1]])

[Package dcanr version 1.8.0 Index]