subDiffEx: Passes the output of generateSimulatedData() to differential...

Description Usage Arguments Value Functions Examples

View source: R/DownsampleMatrix.R

Description

Passes the output of generateSimulatedData() to differential expression tests, picking either t-tests or ANOVA for data with only two conditions or multiple conditions, respectively.

Usage

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subDiffEx(tempData)

subDiffExttest(countMatrix, class.labels, test.type = "t.equalvar")

subDiffExANOVA(countMatrix, condition)

Arguments

tempData

Matrix. The output of generateSimulatedData(), where the first row contains condition labels.

countMatrix

Matrix. A simulated counts matrix, sans labels.

class.labels

Factor. The condition labels for the simulated cells. Will be coerced into 1's and 0's.

test.type

Type of test to perform. The default is t.equalvar.

condition

Factor. The condition labels for the simulated cells.

Value

subDiffEx(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.

subDiffExttest(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.

subDiffExANOVA(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.

Functions

Examples

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data("mouseBrainSubsetSCE")
res <- generateSimulatedData(
         totalReads = 1000, cells=10,
         originalData = assay(mouseBrainSubsetSCE, "counts"),
         realLabels = colData(mouseBrainSubsetSCE)[, "level1class"])
tempSigDiff <- subDiffEx(res)

data("mouseBrainSubsetSCE")
#sort first 100 expressed genes
ord <- rownames(mouseBrainSubsetSCE)[
  order(rowSums(assay(mouseBrainSubsetSCE, "counts")), 
        decreasing = TRUE)][1:100]
#subset to those first 100 genes
subset <- mouseBrainSubsetSCE[ord, ]
res <- generateSimulatedData(totalReads = 1000, cells=10,
                             originalData = assay(subset, "counts"),
                             realLabels = colData(subset)[, "level1class"])
realLabels <- res[1, ]
output <- res[-1, ]
fdr <- subDiffExttest(output, realLabels)

data("mouseBrainSubsetSCE")
#sort first 100 expressed genes
ord <- rownames(mouseBrainSubsetSCE)[
  order(rowSums(assay(mouseBrainSubsetSCE, "counts")), 
        decreasing = TRUE)][1:100]
# subset to those first 100 genes
subset <- mouseBrainSubsetSCE[ord, ]
res <- generateSimulatedData(totalReads = 1000, cells=10,
                             originalData = assay(subset, "counts"),
                             realLabels = colData(subset)[, "level2class"])
realLabels <- res[1, ]
output <- res[-1, ]
fdr <- subDiffExANOVA(output, realLabels)

singleCellTK documentation built on June 26, 2018, 10 a.m.