getSimDataFrame {BrainSABER} | R Documentation

## Get Age, Structure Acronym, and Similarity Scores Data Frame

### Description

This function takes in a CellScabbard object, or both a similarity vector or data frame as returned by `getSimScores` and a subset of AIBSARNA as returned by `getRelevantGenes`. Constructs a `data.frame` with columns age, structure_acroynym, and either cosine_similarity or euclidean_similarity, sorted by similarity score in decreasing order. In the case of a similarity data frame, a list of data frames is returned.

### Usage

    getSimDataFrame(
      data = NULL,
      sim_score = NULL,
      relevantGenes = NULL,
      similarity_method = "cosine"
    )

### Arguments

`data` | a CellScabbard object with non-empty relevantGenes and similarityScores slots, or a SummarizedExperiment created using the `getRelevantGenes()` function
`sim_score` | a vector or data frame of similarity scores
`relevantGenes` | a SummarizedExperiment object created using the `getRelevantGenes()` function
`similarity_method` | currently supported similarity methods are "cosine" and "euclidean", defaults to "cosine"

### Value

a three-column data.frame or list of data frames

### Examples

    AIBSARNA <- buildAIBSARNA(mini = TRUE)
    # Example 1 - using CellScabbard class
    # get a random sample of 3 genes
    totalGenes <- nrow(AIBSARNA)
    gene_idx <- sample.int(totalGenes, 3)
    sample_idx <- c(1,3,5)
    # Subset AIBSARNA
    exprs <- assay(AIBSARNA)[gene_idx, sample_idx]
    fd <- rowData(AIBSARNA)[gene_idx, ]
    pd <- colData(AIBSARNA)[sample_idx, ]
    # build a trimmed data set
    myGenes <- CellScabbard(exprsData = exprs, phenoData = pd, featureData = fd,
                            AIBSARNA = AIBSARNA, autoTrim = TRUE)
    # cosine similarity method
    similarityScores(myGenes) <- getSimScores(data = myGenes,
                                              similarity_method = "cosine")
    similarityDFs(myGenes) <- getSimDataFrame(data = myGenes,
                                              similarity_method = "cosine")
    similarityDFs(myGenes)
    # euclidean similarity method
    similarityScores(myGenes) <- getSimScores(data = myGenes,
                                              similarity_method = "euclidean")
    similarityDFs(myGenes) <- getSimDataFrame(data = myGenes,
                                              similarity_method = "euclidean")
    similarityDFs(myGenes)

    # Example 2 - manual gene selection and relevant gene extraction
    myGenes <- c(4.484885, 0.121902, 0.510035)
    names(myGenes) <- c("TSPAN6", "DPM1", "C1orf112")
    myGeneSet <- getRelevantGenes(myGenes, AIBSARNA = AIBSARNA,
        AIBSARNAid = "gene_symbol")
    myCosScore <- getSimScores(myGenes, relevantGenes = myGeneSet,
                               similarity_method = "cosine")
    myEucScore <- getSimScores(myGenes, relevantGenes = myGeneSet,
        similarity_method = "euclidean")
    myCosineDF <- getSimDataFrame(sim_score = myCosScore,
                                  relevantGenes = myGeneSet,
                                  similarity_method = "cosine")
    myEuclideanDF <- getSimDataFrame(sim_score = myEucScore,
                                     relevantGenes = myGeneSet,
                                     similarity_method = "euclidean")

[Package BrainSABER version 1.0.0 Index]