`subDiffEx.Rd`

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)

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

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.

`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.

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)