tna.get: Get information from individual slots in a TNA object.

In RTN: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons

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)

RTN documentation built on Nov. 12, 2020, 2:02 a.m.