MAST: MAST

In mmkhan19/singleCellTK: Interactive Analysis of Single Cell RNA-Seq Data

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)

mmkhan19/singleCellTK documentation built on March 22, 2022, 7:43 a.m.