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 1.2.0 Index]