tna.get {RTN} | R Documentation

## Get information from individual slots in a TNA object.

### Description

Get information from individual slots in a TNA object. Available results from a previous analysis can be selected either by pvalue cutoff (default) or top significance.

### Usage

    tna.get(object, what="summary", order=TRUE, ntop=NULL, reportNames=TRUE,
    idkey=NULL)

### Arguments

`object` | an object of class `TNA-class`.
`what` | a single character value specifying which information should be retrieved from the slots. Options: 'summary', 'status', 'para', 'pheno', 'hits', 'regulatoryElements', 'tnet', 'refnet', 'regulons', 'refregulons', 'regulons.and.mode', 'refregulons.and.mode', 'rowAnnotation', 'colAnnotation', 'mra', 'gsea1', 'gsea2', 'gsea2summary'.
`order` | a single logical value specifying whether or not the output data should be ordered by significance. Valid only for 'mra', 'gsea1' and 'gsea2' options.
`ntop` | a single integer value specifying to select how many results of top significance from 'mra', 'gsea1' and 'gsea2' options.
`reportNames` | a single logical value specifying to report regulons with 'names' (when reportNames=TRUE) or not (when reportNames=FALSE). This option is effective only if transcription factors were named with alternative identifiers in the pre-processing analysis. It takes effect on 'mra', 'gsea1' and 'gsea2' options.
`idkey` | an optional single character value specifying an ID name from the available 'TNA' annotation to be used as alias for data query outputs (obs. it has no effect on consolidated tables).

### Details

Options for the 'what' argument retrieve the following types of information:

summary

  A list summarizing parameters and results available in the TNA object.

status

  A vector indicating the status of each available method in the pipeline.

para

  A list with the parameters used by each available method in the pipeline.

pheno

  A numeric vector of phenotypes named by gene identifiers (see `tni2tna.preprocess`).

hits

  A character vector of gene identifiers for those considered as hits (see `tni2tna.preprocess`).

regulatoryElements

  A vector of regulatory elements (e.g. transcription factors).

tnet

  A data matrix with MI values, evaluated by the DPI filter. MI values are computed between regulators and targets, with regulators on cols and targets on rows. Note that signals (+/-) are assigned to the inferred associations in order to represent the 'mode of action', which is derived from Pearson's correlation between regulators and targets.

refnet

  A data matrix with MI values (not evaluated by the DPI filter). MI values are computed between regulators and targets, with regulators on cols and targets on rows. Note that signals (+/-) are assigned to the inferred associations in order to represent the 'mode of action', which is derived from Pearson's correlation between regulators and targets.

regulons

  A list with regulons extracted from the 'tnet' data matrix.

refregulons

  A list with regulons extracted from the 'refnet' data matrix.

regulons.and.mode

  A list with regulons extracted from the 'tnet' data matrix, including the assiged 'mode of action'.

refregulons.and.mode

  A list with regulons extracted from the 'refnet' data matrix, including the assiged 'mode of action'.

rowAnnotation

  A data frame with probe-to-gene annotation.

colAnnotation

  A data frame with sample annotation.

mra

  A data frame with results from the `tna.mra` analysis pipeline.

gsea1

  A data frame with results from the `tna.gsea1` analysis pipeline.

gsea2

  A data frame with results from the `tna.gsea2` analysis pipeline.

### Value

Get the slot content from a `TNA-class` object.

### Author(s)

Mauro Castro

### Examples

    data(tniData)
    data(tnaData)

    ## Not run:

    rtni <- tni.constructor(expData=tniData$expData,
            regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"),
            rowAnnotation=tniData$rowAnnotation)
    rtni <- tni.permutation(rtni)
    rtni <- tni.bootstrap(rtni)
    rtni <- tni.dpi.filter(rtni)
    rtna <- tni2tna.preprocess(rtni, phenotype=tnaData$phenotype,
            hits=tnaData$hits, phenoIDs=tnaData$phenoIDs)

    # run MRA analysis pipeline
    rtna <- tna.mra(rtna)

    # check summary
    tna.get(rtna,what="summary")

    # get results, e.g., from the MRA analysis
    tna.get(rtna,what="mra")

    ## End(Not run)

[Package RTN version 2.18.0 Index]