MAST {singleCellTK} | R Documentation

## MAST

### Description

Run and visualize MAST analysis on a SCtkExperiment object.

### Usage

    MAST(inSCE, condition = NULL, interest.level = NULL,
      freqExpressed = 0.1, fcThreshold = log2(1.5), p.value = 0.05,
      useThresh = FALSE, useAssay = "logcounts")

    thresholdGenes(inSCE, useAssay = "logcounts")

    MASTviolin(inSCE, useAssay = "logcounts", fcHurdleSig, samplesize = 49,
      threshP = FALSE, condition)

    MASTregression(inSCE, useAssay = "logcounts", fcHurdleSig,
      samplesize = 49, threshP = FALSE, condition)

### Arguments

`inSCE` | Input SCtkExperiment object. Required
`condition` | select variable (from the colData) that is used for the model.
`interest.level` | If the condition of interest has more than two factors, indicate which level should be used to compare to all other samples.
`freqExpressed` | Filter genes that are expressed in at least this fraction of cells. The default is expression in 0.1 of samples.
`fcThreshold` | Minimum fold change for differentially expressed gene.
`p.value` | p values for selecting the hurdle result, default is 0.05
`useThresh` | Use adaptive thresholding to filter genes. The default is FALSE.
`useAssay` | The assay to use for the MAST calculations. The default is "logcounts"
`fcHurdleSig` | The filtered result from hurdle model
`samplesize` | The number of most significant genes
`threshP` | Plot threshold values from adaptive thresholding. Default is FALSE

### Value

MAST(): A data.frame of differentially expressed genes with p-values.

thresholdGenes(): list of thresholded counts (on natural scale), thresholds, bins, densities estimated on each bin, and the original data from MAST::thresholdSCRNACountMatrix

MASTviolin(): A ggplot object of MAST violin plots.

MASTregression(): A ggplot object of MAST linear regression plots.

### Functions

- `MAST`: Run MAST analysis.

- `thresholdGenes`: Identify adaptive thresholds

- `MASTviolin`: Visualize MAST results using violin plots

- `MASTregression`: Visualize MAST results using linear model plots

### Examples

    data("mouseBrainSubsetSCE")
    res <- thresholdGenes(mouseBrainSubsetSCE)

[Package singleCellTK version 1.6.0 Index]