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.

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

  • subDiffEx: Get PCA components for a SCtkE object

  • subDiffExttest: Runs t-tests on all genes in a simulated dataset with 2 conditions, and adjusts for FDR.

  • subDiffExANOVA: Runs ANOVA on all genes in a simulated dataset with more than 2 conditions, and adjusts for FDR.

Examples

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)