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

DownsampleCells(originalData, useAssay = "counts", minCountDetec = 10,
minCellsDetec = 3, minCellnum = 10, maxCellnum = 1000, realLabels,
depthResolution = 10, iterations = 10, totalReads = 1e+06)

## Arguments

originalData SCtkExperiment. The SCtkExperiment object storing all assay data from the shiny app. Character. The name of the assay to be used for subsampling. Numeric. The minimum number of reads found for a gene to be considered detected. Numeric. The minimum number of cells a gene must have at least 1 read in for it to be considered detected. Numeric. The minimum number of virtual cells to include in the smallest simulated dataset. Numeric. The maximum number of virtual cells to include in the largest simulated dataset Character. The name of the condition of interest. Must match a name from sample data. If only two factors present in the corresponding colData, will default to t-test. If multiple factors, will default to ANOVA. 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. Numeric. How many times should each experimental design be simulated? Numeric. How many aligned reads to put in each simulated dataset.

## 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 <- DownsampleCells(subset,
realLabels = "level1class",
iterations=2)