Run and visualize MAST analysis on a SCtkExperiment object.
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 )
Input SCtkExperiment object. Required
select variable (from the colData) that is used for the model.
If the condition of interest has more than two factors, indicate which level should be used to compare to all other samples.
Filter genes that are expressed in at least this fraction of cells. The default is expression in 0.1 of samples.
Minimum fold change for differentially expressed gene.
p values for selecting the hurdle result, default is 0.05
Use adaptive thresholding to filter genes. The default is FALSE.
The assay to use for the MAST calculations. The default is "logcounts"
The filtered result from hurdle model
The number of most significant genes
Plot threshold values from adaptive thresholding. Default is FALSE
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.
MAST: Run MAST analysis.
thresholdGenes: Identify adaptive thresholds
MASTviolin: Visualize MAST results using violin plots
MASTregression: Visualize MAST results using linear model plots
data("mouseBrainSubsetSCE") res <- thresholdGenes(mouseBrainSubsetSCE) #> `fData` has no primerid. I'll make something up. #> `cData` has no wellKey. I'll make something up. #> Assuming data assay in position 1, with name et is log-transformed.