mergeGroups {GRridge} | R Documentation

## Merge groups in a partition

### Description

Pathway-based partition often contains a considerable number of gene sets (or groups). This function merges groups resulted by `matchGeneSets`. The first principal component in each group is calculated. Hierarchical clustering analysis is then performed on the first principal components from all groups. Important Note: re-grouping is only done in the non-reminder group.

### Usage

    mergeGroups(highdimdata, initGroups=initGroups,maxGroups=maxGroups,
                        methodDistance="manhattan", methodClust="complete")

### Arguments

`highdimdata` | Matrix or numerical data frame. Contains the primary data of the study. Columns are samples, rows are features.
`initGroups` | A list of initial given groups resulted from `matchGeneSets`.
`maxGroups` | Numeric. The new desired number of groups.
`methodDistance` | The distance method used for clustering. See `dist` for futher options. Default: manhattan distance.
`methodClust` | The agglomeration method used for Grouping. See `hclust` for further options. Default: complete.

### Value

A list object containing:

`newGroups` | A list the components of which contain the indices of the features belonging to each of the group. This object is the same as the object created by `matchGeneSets` and `CreatePartition`
`newGroupMembers` | A list of members in the new merged groups.

### Author(s)

Putri W. Novianti

### See Also

Creating partitions: `CreatePartition`. Creating partitions based on overlaping groups (gene sets/pathways): `matchGeneSets`.

### Examples

    # Source: http://software.broadinstitute.org/gsea/msigdb/collections.jsp
    # A GMT file containing information about transcription factor targets should
    # be downloaded first from the aforementioned source.
    # Section C3: motif gene sets; subsection: transcription factor targets;
    # file: "c3.tft.v5.0.symbols.gmt"
    # Details of the gene sets:
    # Gene sets contain genes that share a transcription factor binding site
    # defined in the TRANSFAC (version 7.4, http://www.gene-regulation.com/) database.
    # Each of these gene sets is annotated by a TRANSFAC record.

    # Load data objects
    data(dataWurdinger)

    # Transform the data set to the square root scale
    dataSqrtWurdinger <- sqrt(datWurdinger_BC)

    #Standardize the transformed data
    datStdWurdinger <- t(apply(dataSqrtWurdinger,1,function(x){(x-mean(x))/sd(x)}))

    # A list of gene names in the primary RNAseq data
    genesWurdinger <- as.character(annotationWurdinger$geneSymbol)

    ## Creating partitions based on pathways information (e.g. GSEA object)
    ## Some variables may belong to more than one groups (gene sets).
    ## The argument minlen=25 implies the minimum number of members in a gene set
    ## If remain=TRUE, gene sets with less than 25 members are grouped to the
    ## "remainder" group.
    ## The "TFsym" is available on https://github.com/markvdwiel/GRridgeCodata
    # gseTF <- matchGeneSets(genesWurdinger,TFsym,minlen=25,remain=TRUE)

    ## Regrouping gene sets by hierarchical clustering analysis.
    ## The number of gene sets from the GSEA database is relatively too high to be used
    ## in the GRridge model. Here, the initial gene sets are re-grouped into maxGroups=5, using
    ## information from the primary data set.
    # gseTF_newGroups <- mergeGroups(highdimdata=datStdWurdinger, initGroups =gseTF, maxGroups=5);

    ## Extracting indices of new groups
    ## This following object (gseTF2) can be used further as an input
    ## in the "partitions" argument in the "grridge" function
    # gseTF2 <- gseTF_newGroups$newGroups

    ## Members of the new groups
    # newGroupMembers <- gseTF_newGroups$newGroupMembers

[Package GRridge version 1.6.0 Index]