subDiffEx {singleCellTK} | R Documentation

## 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.

### 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

    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`:

- `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)][seq(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)][seq(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)

[Package singleCellTK version 2.2.0 Index]