Description Usage Arguments Value Functions Examples
Run and visualize MAST analysis on a SCtkExperiment object.
1 2 3 4 5 6 7 8 9 10 11  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)

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 
MAST(): A data.frame of differentially expressed genes with pvalues.
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.
MAST
: Run MAST analysis.
thresholdGenes
: Identify adaptive thresholds
MASTviolin
: Visualize MAST results using violin plots
MASTregression
: Visualize MAST results using linear model plots
1 2  data("mouseBrainSubsetSCE")
res < thresholdGenes(mouseBrainSubsetSCE)

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