FindGeneMarkers {ILoReg} | R Documentation

## Identification of gene markers for a cluster or two arbitrary combinations of clusters

### Description

FindGeneMarkers enables identifying gene markers for one cluster or two arbitrary combinations of clusters, e.g. 1_2 vs. 3_4_5. Gene and cell filters can be applied to accelerate the analysis, but this might lead to missing weak signals.

### Usage

    FindGeneMarkers.SingleCellExperiment(
      object,
      clusters.1,
      clusters.2,
      clustering.type,
      test,
      logfc.threshold,
      min.pct,
      min.diff.pct,
      min.cells.group,
      max.cells.per.cluster,
      pseudocount.use,
      return.thresh,
      only.pos
    )

    ## S4 method for signature 'SingleCellExperiment'
    FindGeneMarkers(
      object,
      clusters.1 = NULL,
      clusters.2 = NULL,
      clustering.type = "",
      test = "wilcox",
      logfc.threshold = 0.25,
      min.pct = 0.1,
      min.diff.pct = NULL,
      min.cells.group = 3,
      max.cells.per.cluster = NULL,
      pseudocount.use = 1,
      return.thresh = 0.01,
      only.pos = FALSE
    )

### Arguments

`object` | of `SingleCellExperiment` class
`clusters.1` | a character or numeric vector denoting which clusters to use in the first group (named group.1 in the results)
`clusters.2` | a character or numeric vector denoting which clusters to use in the second group (named group.2 in the results)
`clustering.type` | "manual" or "optimal". "manual" refers to the clustering formed using the "SelectKClusters" function and "optimal" to the clustering formed using the "CalcSilhInfo" function. Default is "manual".
`test` | Which test to use. Only "wilcoxon" (the Wilcoxon rank-sum test, AKA Mann-Whitney U test) is supported at the moment.
`logfc.threshold` | Filters out genes that have log2 fold-change of the averaged gene expression values (with the pseudo-count value added to the averaged values before division if pseudocount.use > 0) below this threshold. Default is `0.25`.
`min.pct` | Filters out genes that have dropout rate (fraction of cells expressing a gene) below this threshold in both comparison groups Default is `0.1`.
`min.diff.pct` | Filters out genes that do not have this minimum difference in the dropout rates (fraction of cells expressing a gene) between the two comparison groups. Default is `NULL`.
`min.cells.group` | The minimum number of cells in the two comparison groups to perform the DE analysis. If the number of cells is below the threshold, then the DE analysis is not performed. Default is `3`.
`max.cells.per.cluster` | The maximun number of cells per cluster if downsampling is performed to speed up the DE analysis. Default is `NULL`, i.e. no downsampling.
`pseudocount.use` | A positive integer, which is added to the average gene expression values before calculating the fold-change. This makes sure that no divisions by zero occur. Default is `1`.
`return.thresh` | If only.pos=TRUE, then return only genes that have the adjusted p-value (adjusted by the Bonferroni method) below or equal to this threshold. Default is `0.01`.
`only.pos` | Whether to return only genes that have an adjusted p-value (adjusted by the Bonferroni method) below or equal to the threshold. Default is `FALSE`.

### Value

a data frame of the results if positive results were found, else NULL

### Examples

    library(SingleCellExperiment)
    sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
    sce <- PrepareILoReg(sce)
    ## These settings are just to accelerate the example, use the defaults.
    sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
    sce <- RunPCA(sce,p=5)
    sce <- HierarchicalClustering(sce)
    sce <- SelectKClusters(sce,K=5)
    gene_markes_1 <- FindGeneMarkers(sce,clusters.1=1)
    gene_markes_1_vs_2 <- FindGeneMarkers(sce,clusters.1=1,clusters.2=2)

[Package ILoReg version 1.2.0 Index]