multisim {Onassis} | R Documentation

## `multisim`

### Description

This method computes the semantic similarity between samples annotated with different ontology terms from different ontologies

### Usage

    multisim(similarities, annotations, sample1, sample2, aggregating_function)

    ## S4 method for signature 'list,list,character,character'
    multisim(similarities,
      annotations, sample1, sample2, aggregating_function = "mean")

### Arguments

`similarities` | a list of Similarity instances, one for each ontology used to annotate the data
`annotations` | a list of annotated data frames obtained using annotateDF or findEntities, one for each ontology
`sample1` | the name of a sample in annotations
`sample2` | the name of a sample in annotations
`aggregating_function` | A function used to aggregate the single similarities obtained from each ontology annotation. The function should be applied to a numeric vector. The default value is 'mean'

### Value

The aggregate semantic similarity between the samples sample1 and sample2

### Examples

    ef <- new('EntityFinder')

    opts <- CMoptions()
    cell_dict_file <- system.file('extdata', 'sample.cs.obo', package='OnassisJavaLibs')
    sample_dict <- CMdictionary(cell_dict_file, outputDir=getwd(),
    synonymType='ALL')
    samples <- findEntities(ef, system.file('extdata', 'test_samples',
    'test_samples.txt',
    package='Onassis'), outDir=getwd(), multipleDocs=TRUE, configOpt=opts,
     cmDict=sample_dict)
    d_dict_file <-  system.file('extdata', 'sample.do.obo', package='OnassisJavaLibs')
    disease_dict <- CMdictionary(d_dict_file, outputDir=getwd(), synonymType='ALL')
    disease <- findEntities(ef, system.file('extdata', 'test_samples',
    'test_samples.txt', package='Onassis'),
     multipleDocs=TRUE, outDir=getwd(), configOpt=opts,
      cmDict=disease_dict)

    cell_sim <- new('Similarity')
    ontology(cell_sim) <- cell_dict_file

    disease_sim <- new('Similarity')
    ontology(disease_sim) <- d_dict_file

    pairwiseConfig(cell_sim) <- listSimilarities()$pairwiseMeasures[9]
    pairwiseConfig(disease_sim) <- listSimilarities()$pairwiseMeasures[9]
    groupConfig(cell_sim) <- listSimilarities()$groupwiseMeasures[3]
    groupConfig(disease_sim) <- listSimilarities()$groupwiseMeasures[3]
    similarity <- multisim(list(cell_sim, disease_sim),
    list(samples, disease),
    as.character(as.vector(samples[1,1])),
    as.character(as.vector(samples[5,1])), 'mean')

[Package Onassis version 1.6.1 Index]