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