diffAbundanceFET {singleCellTK} | R Documentation

## Calculate Differential Abundance with FET

### Description

Calculate Differential Abundance with FET

### Usage

    diffAbundanceFET(inSCE, cluster, variable, control, case, analysisName)

### Arguments

`inSCE` | A `SingleCellExperiment` object.
`cluster` | A single `character`, specifying the name to store the cluster label in `colData`.
`variable` | A single `character`, specifying the name to store the phenotype labels in `colData`.
`control` | `character`. Specifying one or more categories that can be found in the vector specified by `variable`.
`case` | `character`. Specifying one or more categories that can be found in the vector specified by `variable`.
`analysisName` | A single `character`. Will be used for naming the result table, which will be saved in metadata slot.

### Details

This function will calculate the cell counting and fraction by dividing all cells to groups specified by the arguments, together with statistical summary by performing Fisher Exact Tests (FET).

### Value

The original `SingleCellExperiment` object with `metadata(inSCE)` updated with a list `diffAbundanceFET`, containing a new `data.frame` for the analysis result, named by `analysisName`. The `data.frame` contains columns for number and fraction of cells that belong to different cases, as well as "Odds_Ratio", "PValue" and "FDR".

### Examples

    data("mouseBrainSubsetSCE", package = "singleCellTK")
    mouseBrainSubsetSCE <- diffAbundanceFET(inSCE = mouseBrainSubsetSCE,
                                                    cluster = "tissue",
                                                    variable = "level1class",
                                                    case = "oligodendrocytes",
                                                    control = "microglia",
                                                    analysisName = "diffAbundFET")

[Package singleCellTK version 2.2.0 Index]