DownsampleDepth {singleCellTK} | R Documentation

## Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size

### Description

Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size

### Usage

    DownsampleDepth(originalData, useAssay = "counts", minCount = 10,
      minCells = 3, maxDepth = 1e+07, realLabels, depthResolution = 10,
      iterations = 10)

### Arguments

`originalData` | SCtkExperiment. The SCtkExperiment object storing all assay data from the shiny app.
`useAssay` | Character. The name of the assay to be used for subsampling.
`minCount` | Numeric. The minimum number of reads found for a gene to be considered detected.
`minCells` | Numeric. The minimum number of cells a gene must have at least 1 read in for it to be considered detected.
`maxDepth` | Numeric. The highest number of total reads to be simulated.
`realLabels` | Character. The name of the condition of interest. Must match a name from sample data.
`depthResolution` | Numeric. How many different read depth should the script simulate? Will simulate a number of experimental designs ranging from 10 reads to maxReadDepth, with logarithmic spacing.
`iterations` | Numeric. How many times should each experimental design be simulated?

### Value

A 3-dimensional array, with dimensions = c(iterations, depthResolution, 3). [,,1] contains the number of detected genes in each simulated dataset, [,,2] contains the number of significantly differentially expressed genes in each simulation, and [,,3] contains the mediansignificant effect size in each simulation. If no genes are significantly differentially expressed, the median effect size defaults to infinity.

### Examples

    data("mouseBrainSubsetSCE")
    subset <- mouseBrainSubsetSCE[1:1000,]
    res <- DownsampleDepth(subset,
                           realLabels = "level1class",
                           iterations=2)

[Package singleCellTK version 1.6.0 Index]