findMarkerDiffExp {singleCellTK} | R Documentation

## Find the marker gene set for each cluster With an input SingleCellExperiment object and specifying the clustering labels, this function iteratively call the differential expression analysis on each cluster against all the others.

### Description

Find the marker gene set for each cluster With an input SingleCellExperiment object and specifying the clustering labels, this function iteratively call the differential expression analysis on each cluster against all the others.

### Usage

    findMarkerDiffExp(
      inSCE,
      useAssay = "logcounts",
      method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"),
      cluster = "cluster",
      covariates = NULL,
      log2fcThreshold = 0.25,
      fdrThreshold = 0.05,
      minClustExprPerc = 0.6,
      maxCtrlExprPerc = 0.4,
      minMeanExpr = 0.5
    )

### Arguments

`inSCE` | SingleCellExperiment inherited object.
`useAssay` | character. A string specifying which assay to use for the MAST calculations. Default `"logcounts"`.
`method` | A single character for specific differential expression analysis method. Choose from `'wilcox'`, `'MAST'`, `'DESeq2'`, `'Limma'`, and `'ANOVA'`. Default `"wilcox"`.
`cluster` | One single character to specify a column in `colData(inSCE)` for the clustering label. Alternatively, a vector or a factor is also acceptable. Default `"cluster"`.
`covariates` | A character vector of additional covariates to use when building the model. All covariates must exist in `names(colData(inSCE))`. Not applicable when `method` is `"MAST"` method. Default `NULL`.
`log2fcThreshold` | Only out put DEGs with the absolute values of log2FC larger than this value. Default `NULL`
`fdrThreshold` | Only out put DEGs with FDR value smaller than this value. Default `1`
`minClustExprPerc` | A numeric scalar. The minimum cutoff of the percentage of cells in the cluster of interests that expressed the marker gene. Default `0.7`.
`maxCtrlExprPerc` | A numeric scalar. The maximum cutoff of the percentage of cells out of the cluster (control group) that expressed the marker gene. Default `0.4`.
`minMeanExpr` | A numeric scalar. The minimum cutoff of the mean expression value of the marker in the cluster of interests. Default `1`.

### Value

The input SingleCellExperiment object with `metadata(inSCE)$findMarker` updated with a data.table of the up- regulated DEGs for each cluster.

### Examples

    data("mouseBrainSubsetSCE", package = "singleCellTK")
    mouseBrainSubsetSCE <- findMarkerDiffExp(mouseBrainSubsetSCE,
                                             useAssay = "logcounts",
                                             cluster = "level1class")

[Package singleCellTK version 2.2.0 Index]