Function reference • MAST

Loading, transforming and summarizing Functions to load data, add covariates or annotations, or provide high-level summaries.
`FromFlatDF()`	Construct a SingleCellAssay (or derived subclass) from a `flat` (melted) data.frame/data.table
`FromMatrix()`	Construct a SingleCellAssay from a matrix or array of expression
`SceToSingleCellAssay()`	Coerce a SingleCellExperiment to some class defined in MAST
`convertMASTClassicToSingleCellAssay()`	Convert a MASTClassic SingleCellAssay
`magic_assay_names()` `assay_idx()` `assay(<SingleCellAssay>,<missing>)`	Default `assay` returned
`melt(<SingleCellAssay>)`	"Melt" a `SingleCellAssay` matrix
`zlm()`	Zero-inflated regression for SingleCellAssay
`freq()` `condmean()` `condSd()` `numexp()`	Summary statistics for genes in an experiment
`getConcordance()` `getwss()` `getss()` `getrc()`	Get the concordance between two experiments
`read.fluidigm()`	Reads a Fluidigm Biomark (c. 2011) raw data file (or set of files)
`primerAverage()`	Average expression values for duplicated/redundant genes
`MAST-defunct`	Defunct functions in package `MAST`
Thresholding Adaptive thresholding of background noise.
`thresholdSCRNACountMatrix()`	Threshold a count matrix using an adaptive threshold.
`summary(<thresholdSCRNACountMatrix>)` `print(<summaryThresholdSCRNA>)`	Summarize the effect of thresholding
`plot(<thresholdSCRNACountMatrix>)`	Plot cutpoints and densities for thresholding
Differential Expression Testing with Hurdle Model Fit a Hurdle linear model to test for zero-inflated differential expression.
`zlm()`	Zero-inflated regression for SingleCellAssay
`update(<LMERlike>)` `vcov(<LMERlike>)` `coef(<LMERlike>)` `logLik(<LMERlike>)`	Wrapper for lmer/glmer
`BayesGLMlike-class`	Wrapper for bayesian GLM
`print(<summaryZlmFit>)`	Print summary of a ZlmFit
`ebayes()`	Estimate hyperparameters for hierarchical variance model for continuous component
`defaultPrior()`	Initialize a prior to be used a prior for BayeGLMlike/BayesGLMlike2
Specifying hypothesis and contrasts to test Testing linear functions of coefficients.
`Hypothesis()`	Describe a linear model hypothesis to be tested
`lrTest()`	Run a likelihood-ratio test
`waldTest()`	Run a Wald test
`lrTest(<ZlmFit>,<character>)`	Likelihood ratio test
Low-level manipulation of fitted hurdle models Return coefficients, standard errors, etc. Calculate and return residuals from models.
`se.coef()`	Return coefficient standard errors
`lrTest(<ZlmFit>,<CoefficientHypothesis>)` `lrTest(<ZlmFit>,<Hypothesis>)` `lrTest(<ZlmFit>,<matrix>)` `waldTest(<ZlmFit>,<CoefficientHypothesis>)` `waldTest(<ZlmFit>,<Hypothesis>)` `coef(<ZlmFit>)` `vcov(<ZlmFit>)` `se.coef(<ZlmFit>)`	An S4 class to hold the output of a call to zlm
`predict(<ZlmFit>)`	Return predictions from a ZlmFit object.
`logFC()` `getLogFC()`	Calculate log-fold changes from hurdle model components
`collectResiduals()` `discrete_residuals_hook()` `continuous_residuals_hook()` `combined_residuals_hook()` `deviance_residuals_hook()` `fitted_phat()` `partialScore()`	Residual hooks and collection methods
`impute()`	impute missing continuous expression for plotting
Gene set enrichment testing Tests on the average differential expression effect in a gene set, accounting for gene-gene correlations. Uses bootstraps to assess the gene-gene correlations.
`gseaAfterBoot()` `gsea_control()`	Gene set analysis for hurdle model
`pbootVcov1()` `bootVcov1()`	Bootstrap a zlmfit
`summary(<GSEATests>)`	Summarize gene set enrichment tests
Plotting methods Miscellaneous plotting methods. Concordance, PCA biplots, and effects plots for fitted hurdle models.
`myBiplot()`	Makes a nice BiPlot
`plotSCAConcordance()`	Concordance plots of filtered single vs n-cell assays
`stat_ell()`	Plot confidence ellipse in 2D
Legacy methods Older and perhaps obsoleted methods.
`LRT()`	Likelihood Ratio Tests for SingleCellAssays
`computeEtFromCt()`	Compute the Et from the Ct
`mast_filter()` `burdenOfFiltering()`	Filter a SingleCellAssay
`expavg()`	Exponential average
`filterLowExpressedGenes()`	Filter low-expressing genes

Reference

Loading, transforming and summarizing

Thresholding

Differential Expression Testing with Hurdle Model

Specifying hypothesis and contrasts to test

Low-level manipulation of fitted hurdle models

Gene set enrichment testing

Plotting methods

Legacy methods