#' cnr: copy number, rounded
#'
#' @description
#'
#' The cnr object is a list of four relational matrices. The bins, genes,
#' annotation, qc, chromInfo, and gene.index. The structure is inspired by
#' Scanpy's AnnData which cleverly integrates complex data into a simple
#' architecture.
#'
#' The object stores processing results required for data exploration,
#' visualization and genetic analyses. Specifically, the distance matrix,
#' phylogenetic analysis, pseudobulk, and heterogeneity analysis.
#'
#'
#' @format An object class list containg a rounded CNR
#'
#' \itemize{
#'
#' \item X, An integer matrix of bins x n.cells containing copy number
#' estimations for each bin[i] and cell[j] Where bins represent a common genomic
#' segment across all cells (either a fixed with, variable binning, or .seg data).
#' This data can be constructed using a variable length bin and CBS (Varbin
#' algorithm) (Baslan et al 2012.), and implemented on Ginkgo, or from hmmCopy.
#' These are upstream analyses to the package.
#'
#' For mutations in single-cells, the cnq can be a binary incidence (0,1) matrix
#' representing presence or absence of specific mutations, or a ternary (0,1,2)
#' representing genotypes as the number of alternate allele copies
#'
#' We noticed that estimates for deletions and losses don't follow standard
#' numeric rounding. We set a the thresholds of < 0.2 (average quantal estimate
#' of the Y chromosome in females) for deletions (i.e. 0 copies); between 0.2
#' and 1.2 for losses (i.e. 1 copy); between 1.2 and 2.5 for 2 copies, and
#' everything else standard numeric rounding.
#'
#' \item genes, gene copy number interpolation from bins. The genes matrix
#' is an interpolated, transposed, expansion of bins. The expansion is
#' constructed internally using the expand2genes function.
#'
#' \item Y, phenotypic data of single-cells, contains cells as rows, and
#' phentypes in columns. Phenotypes can be information about individual samples,
#' or if same-cell methods were used, the RNA expression from the same cell.
#'
#' \item qc, quality control metrics. This matrix contains additional metadata
#' that is technical, e.g. number of reads, MAPD estimates, and the PASS/FAIL
#' qc.status for individual cells. contains cells as rows and metadata as columns
#'
#' \item chromInfo, ordered chromosome information for the bins
#'
#' \item gene.index, table to map bins to genes
#'
#' \item cells, a list of cells
#'
#' \item bulk, logical, weather the data is bulk DNA or cells. If TRUE, data
#' will not be rounded and it's assumed is log ratio data. If FALSE, data is
#' considered as single-cell and copy numbers are considered as integer copy
#' number. Estimates are rounded to the nearest integer (see above).
#' ...
#' }
#'
#' @return
#'
#' \itemize{
#' \item cdb, pairwise cell dissimilarity using Bray-Curtis
#'
#' \item hcdb, heirarchical clustering of cells based
#'
#' \item phylo, cell phyogenetic tree. Analysis is produced with
#' \code{ape}. Default is "balanced minimum evolution"
#'
#' \item tree.height, height cutoff of the tree, set as intersection between
#' the total number of multi-cell clusters and one-cell clusters
#'
#' \item ccp, output from ConsensusClusterPlus, the first element is a color map.
#' Elements 2:(40) contain 4 outputs: consensusMatrix, consensusTree,
#' consensusClass, ml, and clrs. Each element corresponds to k in k-means
#' clustering
#'
#' \item kStats, spectral analysis of consensus clustering
#'
#' \item eigenVals, spectral analysis eigen values
#'
#' \item optK optimumn k-parameter (kCC), and stable K (sK)
#'
#' \item cluster_heterogeneity, summary metrics of clones/clusters
#'
#' \item uclust, unique clusters with 3 or more cells
#'
#' \item DDRC.df, bin pseudobulk profiles of each clone (or other chosen group)
#'
#' \item DDRC.g, gene pseudobulk profiles
#'
#' \item vdj.cells list of vdj.cells produced by \code{genotype_vdj}
#'
#' \item DDRC.dist, bray curtis disimilarity of clones
#'
#' \item DDRC.phylo, phylogenetic analysis of clones
#' }
#'
#' @docType data
#' @keywords datasets
#' @usage data(cnr)
#' @source \url{https://github.com/SingerLab/gac"}
"cnr"
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