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# Classes, Generics and Methods
#### Generics ####
# @exportMethod print
#setGeneric("print")
# @exportMethod plot
#setGeneric("plot")
setClassUnion("DfNULL", members=c("data.frame", "NULL"))
setClassUnion("CharNULL", members=c("character", "NULL"))
#### Diversity classes ####
#' S4 class defining a clonal abundance curve
#'
#' \code{AbundanceCurve} defines clonal abundance values.
#'
#' @slot abundance data.frame with relative clonal abundance data and confidence intervals,
#' containing the following columns:
#' \itemize{
#' \item \code{group}: group identifier.
#' \item \code{clone_id} or \code{CLONE}: clone identifier.
#' \item \code{p}: relative abundance of the clone.
#' \item \code{lower}: lower confidence inverval bound.
#' \item \code{upper}: upper confidence interval bound.
#' \item \code{rank}: the rank of the clone abundance.
#' }
#' @slot bootstrap data.frame of bootstrapped clonal distributions.
#' @slot clone_by string specifying the name of the clone column.
#' @slot group_by string specifying the name of the grouping column.
#' @slot groups vector specifying the names of unique groups in group column.
#' @slot n numeric vector indication the number of sequences sampled in each group.
#' @slot nboot numeric specifying the number of bootstrap iterations to use.
#' @slot ci confidence interval defining the upper and lower bounds
#' (a value between 0 and 1).
#'
#' @name AbundanceCurve-class
#' @rdname AbundanceCurve-class
#' @aliases AbundanceCurve
#' @exportClass AbundanceCurve
setClass("AbundanceCurve",
slots=c(abundance="data.frame",
bootstrap="data.frame",
clone_by="character",
group_by="character",
groups="character",
n="numeric",
ci="numeric",
nboot="numeric"))
#' S4 class defining a diversity curve
#'
#' \code{DiversityCurve} defines diversity (\eqn{D}) scores over multiple diversity
#' orders (\eqn{Q}).
#'
#' @slot diversity data.frame defining the diversity curve with the following columns:
#' \itemize{
#' \item \code{group}: group label.
#' \item \code{q}: diversity order.
#' \item \code{d}: mean diversity index over all bootstrap
#' realizations.
#' \item \code{d_sd}: standard deviation of the diversity index
#' over all bootstrap realizations.
#' \item \code{d_lower}: diversity lower confidence inverval bound.
#' \item \code{d_upper}: diversity upper confidence interval bound.
#' \item \code{e}: evenness index calculated as \code{D}
#' divided by \code{D} at \code{Q=0}.
#' \item \code{e_lower}: evenness lower confidence inverval bound.
#' \item \code{e_upper}: eveness upper confidence interval bound.
#' }
#' @slot tests data.frame describing the significance test results with columns:
#' \itemize{
#' \item \code{test}: string listing the two groups tested.
#' \item \code{delta_mean}: mean of the \eqn{D} bootstrap delta
#' distribution for the test.
#' \item \code{delta_sd}: standard deviation of the \eqn{D}
#' bootstrap delta distribution for the test.
#' \item \code{pvalue}: p-value for the test.
#' }
#' @slot group_by string specifying the name of the grouping column in diversity calculation.
#' @slot groups vector specifying the names of unique groups in group column in diversity calculation.
#' @slot method string specifying the type of diversity calculated.
#' @slot q vector of diversity hill diversity indices used for computing diversity.
#' @slot n numeric vector indication the number of sequences sampled in each group.
#' @slot ci confidence interval defining the upper and lower bounds
#' (a value between 0 and 1).
#'
#' @name DiversityCurve-class
#' @rdname DiversityCurve-class
#' @aliases DiversityCurve
#' @exportClass DiversityCurve
setClass("DiversityCurve",
slots=c(diversity="data.frame",
tests="DfNULL",
method="character",
group_by="character",
groups="character",
q="numeric",
n="numeric",
ci="numeric"))
#### Diversity methods ####
#' @param x AbundanceCurve object
#'
#' @rdname AbundanceCurve-class
#' @aliases AbundanceCurve-method
#' @export
setMethod("print", c(x="AbundanceCurve"), function(x) { print(x@abundance) })
#' @param y ignored.
#' @param ... arguments to pass to \link{plotDiversityCurve}.
#'
#' @rdname AbundanceCurve-class
#' @aliases AbundanceCurve-method
#' @export
setMethod("plot", c(x="AbundanceCurve", y="missing"),
function(x, y, ...) { plotAbundanceCurve(x, ...) })
#' @param x DiversityCurve object
#'
#' @rdname DiversityCurve-class
#' @aliases DiversityCurve-method
#' @export
setMethod("print", c(x="DiversityCurve"), function(x) { print(x@diversity) })
#' @param y diversity order to plot (q).
#' @param ... arguments to pass to \link{plotDiversityCurve} or \link{plotDiversityTest}.
#'
#' @rdname DiversityCurve-class
#' @aliases DiversityCurve-method
#' @export
setMethod("plot", c(x="DiversityCurve", y="missing"),
function(x, y, ...) { plotDiversityCurve(x, ...) })
#' @rdname DiversityCurve-class
#' @aliases DiversityCurve-method
#' @export
setMethod("plot", c(x="DiversityCurve", y="numeric"),
function(x, y, ...) { plotDiversityTest(x, y, ...) })
#### Lineage classes ####
#' S4 class defining a clone
#'
#' \code{ChangeoClone} defines a common data structure for perform lineage recontruction
#' from Change-O data.
#'
#' @slot data data.frame containing sequences and annotations. Contains the
#' columns \code{SEQUENCE_ID} and \code{SEQUENCE}, as well as any additional
#' sequence-specific annotation columns.
#' @slot clone string defining the clone identifier.
#' @slot germline string containing the germline sequence for the clone.
#' @slot v_gene string defining the V segment gene call.
#' @slot j_gene string defining the J segment gene call.
#' @slot junc_len numeric junction length (nucleotide count).
#'
#' @seealso See \link{makeChangeoClone} and \link{buildPhylipLineage} for use.
#'
#' @name ChangeoClone-class
#' @rdname ChangeoClone-class
#' @aliases ChangeoClone
#' @exportClass ChangeoClone
setClass("ChangeoClone",
slots=c(data="data.frame",
clone="character",
germline="character",
v_gene="character",
j_gene="character",
junc_len="numeric"))
#### Topology classes ####
#' S4 class defining edge significance
#'
#' \code{MRCATest} defines the significance of enrichment for annotations appearing at
#' the MRCA of the tree.
#'
#' @slot tests data.frame describing the significance test results with columns:
#' \itemize{
#' \item \code{annotation}: annotation value.
#' \item \code{count}: observed count of MRCA positions
#' with the given annotation.
#' \item \code{expected}: expected mean count of MRCA occurance
#' for the annotation.
#' \item \code{pvalue}: one-sided p-value for the hypothesis that
#' the observed annotation abundance is greater
#' than expected.
#' }
#' @slot permutations data.frame containing the raw permutation test data with columns:
#' \itemize{
#' \item \code{annotation}: annotation value.
#' \item \code{count}: count of MRCA positions with the
#' given annotation.
#' \item \code{iter}: numerical index define which
#' permutation realization each
#' observation corresponds to.
#' }
#' @slot nperm number of permutation realizations.
#'
#' @name MRCATest-class
#' @rdname MRCATest-class
#' @aliases MRCATest
#' @exportClass MRCATest
setClass("MRCATest",
slots=c(tests="data.frame",
permutations="data.frame",
nperm="numeric"))
#' S4 class defining edge significance
#'
#' \code{EdgeTest} defines the significance of parent-child annotation enrichment.
#'
#' @slot tests data.frame describing the significance test results with columns:
#' \itemize{
#' \item \code{parent}: parent node annotation.
#' \item \code{child}: child node annotation
#' \item \code{count}: count of observed edges with the given
#' parent-child annotation set.
#' \item \code{expected}: mean count of expected edges for the
#' given parent-child relationship.
#' \item \code{pvalue}: one-sided p-value for the hypothesis that
#' the observed edge abundance is greater
#' than expected.
#' }
#' @slot permutations data.frame containing the raw permutation test data with columns:
#' \itemize{
#' \item \code{parent}: parent node annotation.
#' \item \code{child}: child node annotation
#' \item \code{count}: count of edges with the given parent-child
#' annotation set.
#' \item \code{iter}: numerical index define which permutation
#' realization each observation corresponds
#' to.
#' }
#' @slot nperm number of permutation realizations.
#'
#' @name EdgeTest-class
#' @rdname EdgeTest-class
#' @aliases EdgeTest
#' @exportClass EdgeTest
setClass("EdgeTest",
slots=c(tests="data.frame",
permutations="data.frame",
nperm="numeric"))
#### Topology methods ####
#' @param x MRCATest object.
#'
#' @rdname MRCATest-class
#' @aliases MRCATest-method
#' @export
setMethod("print", c(x="MRCATest"), function(x) { print(x@tests) })
#' @param y ignored.
#' @param ... arguments to pass to \link{plotMRCATest}.
#'
#' @rdname MRCATest-class
#' @aliases MRCATest-method
#' @export
setMethod("plot", c(x="MRCATest", y="missing"),
function(x, y, ...) { plotMRCATest(x, ...) })
#' @param x EdgeTest object.
#'
#' @rdname EdgeTest-class
#' @aliases EdgeTest-method
#' @export
setMethod("print", c(x="EdgeTest"), function(x) { print(x@tests) })
#' @param y ignored.
#' @param ... arguments to pass to \link{plotEdgeTest}.
#'
#' @rdname EdgeTest-class
#' @aliases EdgeTest-method
#' @export
setMethod("plot", c(x="EdgeTest", y="missing"),
function(x, y, ...) { plotEdgeTest(x, ...) })
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