MAST: MAST

Description Usage Arguments Value Functions Examples

View source: R/MAST.R

Description

Run and visualize MAST analysis on a SCtkExperiment object.

Usage

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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

Examples

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

singleCellTK documentation built on June 26, 2018, 10 a.m.