armaCor                 armaCor - matrix column correlations. Allows
                        faster matrix correlations with armadillo.
                        Similar to cor() call, will calculate
                        correlation between matrix columns
basicP2proc             Perform basic 'pagoda2' processing, i.e. adjust
                        variance, calculate pca reduction, make knn
                        graph, identify clusters with multilevel, and
                        generate largeVis and tSNE embeddings.
basicP2web              Generate a 'pagoda2' web application from a
                        'Pagoda2' object
buildWijMatrix          Rescale the weights in an edge matrix to match
                        a given perplexity. From 'largeVis',
                        <https://github.com/elbamos/largeVis>
calcMulticlassified     Returns a list vector with the number of cells
                        that are present in more than one selections in
                        the provided p2 selection object
cellsPerSelectionGroup
                        Get the number of cells in each selection group
collapse.aspect.clusters
                        Collapse aspect patterns into clusters
compareClusterings      Compare two different clusterings provided as
                        factors by plotting a normalised heatmap
extendedP2proc          Perform extended 'Pagoda2' processing.
                        Generate organism specific GO environment and
                        calculate pathway overdispersion.
factorFromP2Selection   Returns a factor of cell membership from a p2
                        selection object the factor only includes cells
                        present in the selection. If the selection
                        contains multiclassified cells an error is
                        raised
factorListToMetadata    Converts a list of factors into 'pagoda2'
                        metadata optionally filtering down to the cells
                        present in the provided 'pagoda2' app.
factorToP2selection     Converts a names factor to a p2 selection
                        object if colors are provided it assigns those,
                        otherwise uses a rainbow palette
gene.vs.molecule.cell.filter
                        Filter cells based on gene/molecule dependency
generateClassificationAnnotation
                        Given a cell clustering (partitioning) and a
                        set of user provided selections generate a
                        cleaned up annotation of cluster groups that
                        can be used for classification
get.control.geneset     Get a control geneset for cell scoring using
                        the method described in Puram, Bernstein (Cell,
                        2018)
get.de.geneset          Generate differential expression genesets for
                        the web app given a cell grouping by
                        calculating DE sets between each cell set and
                        everything else
getCellsInSelections    Returns all the cells that are in the
                        designated selections. Given a pagoda2
                        selections object and the names of some
                        selections in it returns the names of the cells
                        that are in these selections removed any
                        duplicates
getClusterLabelsFromSelection
                        Assign names to the clusters, given a
                        clustering vector and a set of selections. This
                        function will use a set of pagoda2 cell
                        seletcion to identify the clusters in a a named
                        factor. It is meant to be used to import user
                        defined annotations that are defined as
                        selections into a more formal categorization of
                        cells that are defined by cluster. To help with
                        this the function allows a percent of cells to
                        have been classified in the selections into
                        multiple groups, something which may be the
                        result of the users making wrong selections.
                        The percent of cells allows to be multiselected
                        in any given group is defined by
                        multiClassCutoff. Furthermore the method will
                        assign each cluster to a selection only if the
                        most popular cluster to the next most popular
                        exceed the ambiguous.ratio in terms of cell
                        numbers. If a cluster does not satisfy this
                        condtiion it is not assigned.
getColorsFromP2Selection
                        Retrieves the colors of each selection from a
                        p2 selection object as a names vector of
                        strings
getIntExtNamesP2Selection
                        Get a mapping form internal to external names
                        for the specified selection object
hierDiffToGenesets      Converts the output of hierarchical
                        differential expression aspects into genesets
                        that can be loaded into a 'pagoda2' web app to
                        retrive the genes that make the geneset
                        interactively
make.p2.app             Generate a Rook Server app from a 'Pagoda2'
                        object.  This generates a 'pagoda2' web object
                        from a 'Pagoda2' object by automating steps
                        that most users will want to run. This function
                        is a wrapper about the 'pagoda2' web
                        constructor.  (Advanced users may wish to use
                        that constructor directly.)
minMaxScale             Scale the designated values between the range
                        of 0 and 1
namedNames              Get a vector of the names of an object named by
                        the names themselves. This is useful with
                        lapply when passing names of objects as it
                        ensures that the output list is also named.
p2.generate.dr.go       Generate a GO environment for human for
                        overdispersion analysis for the the back end
p2.generate.go          Generate a GO environment for the organism
                        specified
p2.generate.human.go    Generate a GO environment for human for
                        overdispersion analysis for the the back end
p2.generate.mouse.go    Generate a GO environment for mouse for
                        overdispersion analysis for the the back end
p2.make.pagoda1.app     Create 'PAGODA1' web application from a
                        'Pagoda2' object 'PAGODA1' found here, with
                        'SCDE':
                        <https://www.bioconductor.org/packages/release/bioc/html/scde.html>
p2.metadata.from.factor
                        Generate a list metadata structure that can be
                        passed to a 'pagoda2' web object constructor as
                        additional metadata given a named factor
p2.toweb.hdea           Generate a 'pagoda2' web object from a
                        'Pagoda2' object using hierarchical
                        differential expression
p2ViewPagodaApp         p2ViewPagodaApp R6 class
pagoda.reduce.loading.redundancy
                        Collapse aspects driven by the same
                        combinations of genes. (Aspects are some
                        pattern across cells e.g. sequencing depth, or
                        PC corresponding to an undesired process such
                        as ribosomal pathway variation.) Examines PC
                        loading vectors underlying the identified
                        aspects and clusters of aspects based on a
                        product of loading and score correlation
                        (raised to corr.power).  Clusters of aspects
                        driven by the same genes are determined based
                        on the parameter "distance.threshold".
pagoda.reduce.redundancy
                        Collapse aspects driven by similar patterns
                        (i.e. separate the same sets of cells) Examines
                        PC loading vectors underlying the identified
                        aspects and clusters aspects based on score
                        correlation. Clusters of aspects driven by the
                        same patterns are determined based on the
                        distance.threshold.
pagoda2WebApp-class     pagoda2WebApp class to create 'pagoda2' web
                        applications via a Rook server
pagoda2WebApp_arrayToJSON
                        pagoda2WebApp_arrayToJSON
pagoda2WebApp_availableAspectsJSON
                        pagoda2WebApp_availableAspectsJSON
pagoda2WebApp_call      pagoda2WebApp_call
pagoda2WebApp_cellOrderJSON
                        pagoda2WebApp_cellOrderJSON
pagoda2WebApp_cellmetadataJSON
                        pagoda2WebApp_cellmetadataJSON
pagoda2WebApp_geneInformationJSON
                        pagoda2WebApp_geneInformationJSON
pagoda2WebApp_generateDendrogramOfGroups
                        Generate a dendrogram of groups
pagoda2WebApp_generateEmbeddingStructure
                        pagoda2WebApp_generateEmbeddingStructure
pagoda2WebApp_generateGeneKnnJSON
                        pagoda2WebApp_generateGeneKnnJSON
pagoda2WebApp_getCompressedEmbedding
                        pagoda2WebApp_getCompressedEmbedding
pagoda2WebApp_packCompressFloat64Array
                        pagoda2WebApp_packCompressFloat64Array
pagoda2WebApp_packCompressInt32Array
                        pagoda2WebApp_packCompressInt32Array
pagoda2WebApp_readStaticFile
                        pagoda2WebApp_readStaticFile
pagoda2WebApp_reducedDendrogramJSON
                        pagoda2WebApp_reducedDendrogramJSON
pagoda2WebApp_serializeToStaticFast
                        pagoda2WebApp_serializeToStaticFast
pagoda2WebApp_serverLog
                        pagoda2WebApp_serverLog
pagoda2WebApp_sparseMatList
                        pagoda2WebApp_sparseMatList
pathway.pc.correlation.distance
                        Calculate correlation distance between PC
                        magnitudes given a number of target dimensions
plotMulticlassified     Plot multiclassified cells per selection as a
                        percent barplot
plotOneWithValues       Plot the embedding of a 'Pagoda2' object with
                        the given values
plotSelectionOverlaps   Get a dataframe and plot summarising overlaps
                        between selection of a pagoda2 selection object
                        ignore self overlaps
projectKNNs             Project a distance matrix into a
                        lower-dimensional space. (from
                        elbamos/largeVis)
read.10x.matrices       Quick loading of 10X CellRanger count matrices
read10xMatrix           This function reads a matrix generated by the
                        10x processing pipeline from the specified
                        directory and returns it. It aborts if one of
                        the required files in the specified directory
                        do not exist.
readPagoda2SelectionAsFactor
                        Read a pagoda2 cell selection file and return
                        it as a factor while removing any
                        mutliclassified cells
readPagoda2SelectionFile
                        Reads a 'pagoda2' web app exported cell
                        selection file exported as a list of list
                        objects that contain the name of the selection,
                        the color (as a hex string) and the identifiers
                        of the individual cells
removeSelectionOverlaps
                        Remove cells that are present in more than one
                        selection from all the selections they are in
score.cells.nb0         Score cells by getting mean expression of genes
                        in signatures
score.cells.puram       Puram, Bernstein (Cell, 2018) Score cells as
                        described in Puram, Bernstein (Cell, 2018)
sgdBatches              Calculate the default number of batches for a
                        given number of vertices and edges. The formula
                        used is the one used by the 'largeVis'
                        reference implementation.  This is
                        substantially less than the recommendation E *
                        10000 in the original paper.
show.app                Directly open the 'pagoda2' web application and
                        view the 'p2web' application object from our R
                        session
subsetSignatureToData   Subset a gene signature to the genes in the
                        given matrix with optional warning if genes are
                        missing
tp2c.view.pathways      View pathway or gene-weighted PCA 'Pagoda2'
                        version of the function pagoda.show.pathways()
                        Takes in a list of pathways (or a list of
                        genes), runs weighted PCA, optionally showing
                        the result.
validateSelectionsObject
                        Validates a pagoda2 selection object
webP2proc               Generate a 'pagoda2' web object
winsorize.matrix        Sets the ncol(mat)*trim top outliers in each
                        row to the next lowest value same for the
                        lowest outliers
writeGenesAsPagoda2Selection
                        Writes a list of genes as a gene selection that
                        can be loaded in the web interface
writePagoda2SelectionFile
                        Writes a pagoda2 selection object as a p2
                        selection file that be be loaded to the web
                        interface
