Initialize the oposSOM pipeline.

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Description

This function initializes the oposSOM environment and sets the preferences.

Usage

1
  opossom.new(preferences)

Arguments

preferences

list with the following optional values:

  • indata: input data matrix containing the expression values or an Biobase::ExpressionSet object (see 'Details' and 'Examples')

  • group.labels: sample assignment to a distinct group, subtype or class (character; "auto" or one label for each sample; may be given with indata ExpressionSet)

  • group.colors: colors of the samples for diverse visualizations (character; one color for each sample; may be given with indata ExpressionSet)

  • dim.1stLvlSom: dimension of primary SOM; use "auto" to apply automatic size estimation (integer, >5)

  • dim.2ndLvlSom: dimensions of second level SOM (integer, >5)

  • training.extension: factor to extend the number of iterations in SOM training (numerical, >0)

  • rotate.SOM.portraits: number of roations of the primary SOM in counter-clockwise fashion (integer {0,1,2,3})

  • flip.SOM.portraits: mirroring the primary SOM along the bottom-left to top-right diagonal (boolean)

  • database.dataset: type of ensemble dataset addressed with biomaRt interface; use "auto" to detect parameter automatically (character)

  • database.id.type: type of rowname identifier in biomaRt database; obsolete if database.dataset="auto" (character)

  • geneset.analysis: perform geneset analysis (boolean)

  • geneset.analysis.exact: enables p-value and fdr calculation in geneset analysis; obsolete if geneset.analysis=F (boolean)

  • standard.spot.modules: spot modules utilized in diverse downstream analyses (character, one of {"overexpression", "group.overexpression", "underexpression", "kmeans", "correlation", "dmap"})

  • spot.coresize.modules: spot detection in summary maps, minimum size (numerical, >0)

  • spot.threshold.modules: spot detection in summary maps, expression threshold (numerical, between 0 and 1)

  • spot.coresize.groupmap: spot detection in group-specific summary maps , minimum size (numerical, >0)

  • spot.threshold.groupmap: spot detection in group-specific summary maps, expression threshold (numerical, between 0 and 1)

  • feature.centralization: enables centralization of the features (boolean)

  • sample.quantile.normalization: enables quantile normalization of the samples (boolean)

  • pairwise.comparison.list: group list for pairwise analyses (list of group lists, see 'Examples') or NULL otherwise

Details

The package then accepts the indata parameter in two formats:<br> Firstly a simple two-dimensional numerical matrix, where the columns and rows represent the samples and genes, respectively. The expression values are usually obtained by calibration and summarization algorithms (e.g. MAS5, VSN or RMA), and transformed into logarithmic scale prior to utilizing them in the pipeline. Secondly the input data can also be given as Biobase::ExpressionSet object. Please check the vignette for more details on the parameters.

Value

A new oposSOM environment which is passed to opossom.run.

Examples

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env <- opossom.new(list(dataset.name="Example",
                        dim.1stLvlSom="auto",
                        dim.2ndLvlSom=10,
                        training.extension=1,
                        rotate.SOM.portraits=0,
                        flip.SOM.portraits=FALSE,
                        database.dataset="auto",
                        geneset.analysis=TRUE,
                        geneset.analysis.exact=TRUE,
                        standard.spot.modules="dmap",
                        spot.coresize.modules=4,
                        spot.threshold.modules=0.9,
                        spot.coresize.groupmap=4,
                        spot.threshold.groupmap=0.7,
                        feature.centralization=TRUE,
                        sample.quantile.normalization=TRUE,
                        pairwise.comparison.list=list(
                          list("groupA"=c("sample1", "sample2"),
                               "groupB"=c("sample3", "sample4")))))


# definition of indata, group.labels and group.colors
env$indata = matrix( runif(1000), 100, 10 )
env$group.labels = c( rep("class 1", 5), rep("class 2", 4), "class 3" )
env$group.colors = c( rep("red", 5), rep("blue", 4), "green" )

# alternative definition of indata, group.labels and group.colors using Biobase::ExpressionSet
library(Biobase)

env$indata = ExpressionSet( assayData=matrix(runif(1000), 100, 10),
                            phenoData=AnnotatedDataFrame(data.frame( 
                                group.labels = c( rep("class 1", 5), rep("class 2", 4), "class 3" ),
                                group.colors = c( rep("red", 5), rep("blue", 4), "green" ) ))
                          )