Project: s41467-025-60900-4 | Function: accordion (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
Return **only** the complete R function code.

user: Complete this R function:

**DESCRIPTION:** Core accordion algorithm for cell cycle phase classification and temporal analysis. Implements accordion methodology for single-cell time-series analysis.

**KEY TERMS:** {
  "accordion": "temporal analysis algorithm",
  "cell cycle": "cellular temporal states"
}

**CONTEXT CODE:** #' @param plot Logical value indicating whether to store plots displaying
#'   detailed annotation information.  This parameter can be set to TRUE only
#'   when \code{include_detailed_annotation_info} is set to TRUE. If TRUE,
#'   lollipop plots displaying the top \code{n_top_markers} group by
#'   \code{group_markers_by} and top \code{n_top_celltypes} for each
#'   \code{annotation_resolution} together with the cell types hierarchies based
#'   on the cell ontology structure are stored in the \code{annotation_name} list. Default
#'   is TRUE.
#' @param color_by Character string specifying if the plot reporting the top
#' cell types for each cluster/cell is colored based on the assigned cell type
#' ("cell_type") or on cluster id ("cluster"). Default is "cell_type.
#'
#' @return A Seurat object or a list
#' @details If a Seurat object was provided in input, the function returns the
#' Seurat object with markers-based scaled data in the scale.data slot and cell
#' types annotation results in the metadata.
#' If \code{include_detailed_annotation_info} and
#' \code{plot} were set to TRUE, a
#' list containing cell types and markers information, together with ggplot
#' objects, is stored in the “misc@\code{annotation_name}” slot. If a count matrix was
#' provided in input, the function returns a list containing the following
#' elements:
#'
#' \describe{
#' \item{"scaled_matrix":}{normalized and scaled expression matrix;}
#' }
#' If \code{annotation_resolution} is set to “cell”:
#' \describe{
#' \item{"cell_annotation":}{data table containing cell types annotation results for each cell;}
#' }
#' If \code{annotation_resolution} is set to “cluster”:
#' \describe{
#' \item{"cluster_annotation":}{data table containing cell types annotation results for each cell;}
#' }
#' If \code{include_detailed_annotation_info} is set to TRUE:
#' \describe{
#' \item{"\code{annotation_name}":}{list containing detailed information of cell types annotation.}
#' }
#' @import scales
#' @import plyr
#' @import data.table
#' @import Seurat
#' @import ggplot2
#' @import stringr
#' @import ontologyIndex
#' @import knitr
#' @importFrom stats aggregate
#' @importFrom methods as
#' @importFrom stats quantile
#' @export
accordion <- function(data,
                      cluster_info = "seurat_clusters",
                      assay = "RNA",
                      database = NULL,
                      CL_celltypes = NULL,
                      species = "Human",
                      tissue = NULL,
                      root_cell_types = NULL,
                      include_descendants = FALSE,
                      ECs_threshold = NULL,
                      SPs_threshold = NULL,
                      log2FC_threshold = NULL,
                      min_n_marker = 5,
                      max_n_marker = 30,
                      combined_score_quantile_threshold = NULL,
                      annotation_resolution = "cluster",
                      cluster_score_quantile_threshold = 0.75,
                      allow_unknown = TRUE,
                      annotation_name = "accordion",
                      include_detailed_annotation_info = TRUE,
                      condition_group_info = NULL,
                      celltype_group_info = NULL,
                      group_markers_by = "celltype_cluster",
                      top_cell_score_quantile_threshold = 0.90,
                      n_top_celltypes = 5,
                      n_top_markers = 5,
                      top_marker_score_quantile_threshold = 0.75,
                      plot = TRUE,
                      color_by = "cell_type") {
  stop("Function implementation has been masked")
}

# Helper functions (to be implemented)
identify_markers <- function(data, min_n_marker, max_n_marker) {
  # Implement marker identification logic
}

annotate_cell_types <- function(data, markers, CL_celltypes, database, species, tissue, 
                                ECs_threshold, SPs_threshold, log2FC_threshold, 
                                combined_score_quantile_threshold) {
  # Implement cell type annotation logic
}

perform_accordion_analysis <- function(cell_annotations, cluster_info, 
                                       cluster_score_quantile_threshold, 
                                       allow_unknown, annotation_resolution) {
  # Implement accordion methodology for temporal analysis
}

aggregate_cluster_results <- function(temporal_results, cluster_info) {
  # Implement aggregation of cluster-level results
}

generate_detailed_annotation <- function(temporal_results, markers, 
                                         condition_group_info, celltype_group_info,
                                         group_markers_by, top_cell_score_quantile_threshold,
                                         n_top_celltypes, n_top_markers, top_marker_score_quantile_threshold) {
  # Implement detailed annotation generation
}

generate_plots <- function(detailed_info, color_by) {
  # Implement plot generation logic
}


Complete the function implementation replacing "stop("Not implemented")".