downSampleCells: Estimate numbers of detected genes, significantly...

In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description

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

Usage

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

Arguments

originalData	The SingleCellExperiment object storing all assay data from the shiny app.
useAssay	Character. The name of the assay to be used for subsampling.
minCountDetec	Numeric. The minimum number of reads found for a gene to be considered detected.
minCellsDetec	Numeric. The minimum number of cells a gene must have at least 1 read in for it to be considered detected.
minCellnum	Numeric. The minimum number of virtual cells to include in the smallest simulated dataset.
maxCellnum	Numeric. The maximum number of virtual cells to include in the largest simulated dataset
realLabels	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.
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?
totalReads	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)

singleCellTK documentation built on Nov. 8, 2020, 5:21 p.m.