ViewPagodaApp-class     A Reference Class to represent the PAGODA
                        application
bwpca                   Determine principal components of a matrix
                        using per-observation/per-variable weights
clean.counts            Filter counts matrix
clean.gos               Filter GOs list
es.mef.small            Sample data
knn                     Sample error model
knn.error.models        Build error models for heterogeneous cell
                        populations, based on K-nearest neighbor cells.
make.pagoda.app         Make the PAGODA app
o.ifm                   Sample error model
pagoda.cluster.cells    Determine optimal cell clustering based on the
                        genes driving the significant aspects
pagoda.effective.cells
                        Estimate effective number of cells based on
                        lambda1 of random gene sets
pagoda.gene.clusters    Determine de-novo gene clusters and associated
                        overdispersion info
pagoda.pathway.wPCA     Run weighted PCA analysis on pre-annotated gene
                        sets
pagoda.reduce.loading.redundancy
                        Collapse aspects driven by the same
                        combinations of genes
pagoda.reduce.redundancy
                        Collapse aspects driven by similar patterns
                        (i.e. separate the same sets of cells)
pagoda.show.pathways    View pathway or gene weighted PCA
pagoda.subtract.aspect
                        Control for a particular aspect of expression
                        heterogeneity in a given population
pagoda.top.aspects      Score statistical significance of gene set and
                        cluster overdispersion
pagoda.varnorm          Normalize gene expression variance relative to
                        transcriptome-wide expectations
pagoda.view.aspects     View PAGODA output
papply                  wrapper around different mclapply mechanisms
pollen                  Sample data
scde                    Single-cell Differential Expression (with
                        Pathway And Gene set Overdispersion Analysis)
scde.browse.diffexp     View differential expression results in a
                        browser
scde.edff               Internal model data
scde.error.models       Fit single-cell error/regression models
scde.expression.difference
                        Test for expression differences between two
                        sets of cells
scde.expression.magnitude
                        Return scaled expression magnitude estimates
scde.expression.prior   Estimate prior distribution for gene expression
                        magnitudes
scde.failure.probability
                        Calculate drop-out probabilities given a set of
                        counts or expression magnitudes
scde.fit.models.to.reference
                        Fit scde models relative to provided set of
                        expression magnitudes
scde.posteriors         Calculate joint expression magnitude posteriors
                        across a set of cells
scde.test.gene.expression.difference
                        Test differential expression and plot
                        posteriors for a particular gene
show.app                View PAGODA application
view.aspects            View heatmap
winsorize.matrix        Winsorize matrix
