R/pipeline.R

In whtns/seuratTools: Tools for Managing Seurat Objects

#' Run Seurat Integration
#'
#' Run batch correction, followed by:
#' 1) stashing of batches in metadata 'batch'
#' 2) clustering with resolution 0.2 to 2.0 in increments of 0.2
#' 3) saving to <proj_dir>/output/sce/<feature>_seu_<suffix>.rds
#'
#' @param suffix a suffix to be appended to a file save in output dir
#' @param seu_list List of seurat objects to be integrated
#' @param resolution Range of resolution
#' @param algorithm Algorithm for modularity optimization. Default 1:original Louvain algorithm
#' @param organism Default "human"
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
#'
#' batches <- panc8 %>%
#'     Seurat::SplitObject(split.by = "tech")
#'
#' integrated_seu <- seurat_integration_pipeline(batches)
seurat_integration_pipeline <- function(seu_list, resolution = seq(0.2, 2.0, by = 0.2), suffix = "", algorithm = 1, organism = "human", annotate_cell_cycle = FALSE, annotate_percent_mito = FALSE, reduction = "pca", find_markers = TRUE, ...) {
    experiment_names <- names(seu_list)

    organisms <- case_when(
        grepl("Hs", experiment_names) ~ "human",
        grepl("Mm", experiment_names) ~ "mouse"
    )

    names(organisms) <- experiment_names

    organisms[is.na(organisms)] <- organism

    integrated_seu <- seurat_integrate(seu_list, organism = organism, ...)

    # cluster merged seurat objects
    if ("harmony" %in% names(integrated_seu@reductions)) reduction <- "harmony"
    integrated_seu <- seurat_cluster(integrated_seu, resolution = resolution, algorithm = algorithm, reduction = reduction, ...)


    seurat_assay <- "gene"
    if ("harmony" %in% names(integrated_seu@reductions)) seurat_assay <- "integrated"

    if (find_markers){
      integrated_seu <- find_all_markers(integrated_seu, seurat_assay = seurat_assay)
    }

    #   enriched_seu <- tryCatch(getEnrichedPathways(integrated_seu), error = function(e) e)
    #   enrichr_available <- !any(class(enriched_seu) == "error")
    #   if(enrichr_available){
    #     integrated_seu <- enriched_seu
    #   }

    # add read count column
    integrated_seu <- add_read_count_col(integrated_seu)

    # annotate cell cycle scoring to seurat objects
    if (annotate_cell_cycle) {
        integrated_seu <- annotate_cell_cycle(integrated_seu, ...)
    }

    # annotate mitochondrial percentage in seurat metadata
    if (annotate_percent_mito) {
        integrated_seu <- add_percent_mito(integrated_seu, ...)
    }

    # annotate excluded cells
    # integrated_seu <- annotate_excluded(integrated_seu, excluded_cells)

    return(integrated_seu)
}

#' Run Seurat Integration
#'
#' Run batch correction, followed by:
#' 1) stashing of batches in metadata 'batch'
#' 2) clustering with resolution 0.2 to 2.0 in increments of 0.2
#' 3) saving to <proj_dir>/output/sce/<feature>_seu_<suffix>.rds
#'
#' @param suffix a suffix to be appended to a file save in output dir
#' @param seu_list List of seurat objects to be integrated
#' @param resolution Range of resolution
#' @param algorithm Algorithm for modularity optimization. Default 1:original Louvain algorithm
#' @param organism Default "human"
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
#'
#' batches <- panc8 %>%
#'     Seurat::SplitObject(split.by = "tech")
#'
#' integrated_seu <- seurat_integration_pipeline(batches)
seurat_v5_integration_pipeline <- function(seu, resolution = seq(0.2, 2.0, by = 0.2), suffix = "", algorithm = 1, organism = "human", annotate_cell_cycle = FALSE, annotate_percent_mito = FALSE, reduction = "pca", ...) {

  integrated_seu <- seurat_v5_integrate(seu, organism = organism, ...)

  # cluster merged seurat objects
  if ("harmony" %in% names(integrated_seu@reductions)) reduction <- "harmony"
  integrated_seu <- seurat_cluster(integrated_seu, resolution = resolution, algorithm = algorithm, reduction = reduction, ...)


  seurat_assay <- "gene"
  if ("harmony" %in% names(integrated_seu@reductions)) seurat_assay <- "integrated"
  integrated_seu <- find_all_markers(integrated_seu, seurat_assay = seurat_assay)

  #   enriched_seu <- tryCatch(getEnrichedPathways(integrated_seu), error = function(e) e)
  #   enrichr_available <- !any(class(enriched_seu) == "error")
  #   if(enrichr_available){
  #     integrated_seu <- enriched_seu
  #   }

  # add read count column
  integrated_seu <- add_read_count_col(integrated_seu)

  # annotate cell cycle scoring to seurat objects
  if (annotate_cell_cycle) {
    integrated_seu <- annotate_cell_cycle(integrated_seu, ...)
  }

  # annotate mitochondrial percentage in seurat metadata
  if (annotate_percent_mito) {
    integrated_seu <- add_percent_mito(integrated_seu, ...)
  }

  # annotate excluded cells
  # integrated_seu <- annotate_excluded(integrated_seu, excluded_cells)

  return(integrated_seu)
}

#' Run Seurat Pipeline
#'
#' This functions allows you to Preprocess, Cluster and Reduce Dimensions for a single seurat object.
#'
#' @param seu A Seurat object
#' @param assay Assay of interest in Seurat object
#' @param resolution Resolution for clustering cells. Default set to 0.6.
#' @param reduction Dimensional reduction object seu
#' @param organism Organism
#' @param ... Extra parameters passed to seurat_process
#'
#' @return
#' @export
#'
#' @examples
#'
#' processed_seu <- seurat_process(panc8)
#'
seurat_process <- function(seu, assay = "gene", resolution = 0.6, reduction = "pca", organism = "human", ...) {
    assays <- names(seu@assays)

    assays <- assays[assays %in% c("gene", "transcript")]

    for (assay in assays) {
        seu[[assay]] <- seurat_preprocess(seu[[assay]], scale = TRUE, ...)
    }

    # PCA
    seu <- seurat_reduce_dimensions(seu, check_duplicates = FALSE, reduction = reduction, ...)

    seu <- seurat_cluster(seu = seu, resolution = resolution, reduction = reduction, ...)

    seu <- find_all_markers(seu, seurat_assay = "gene")

    # if (feature == "gene"){
    #   enriched_seu <- tryCatch(getEnrichedPathways(seu), error = function(e) e)
    #   enrichr_available <- !any(class(enriched_seu) == "error")
    #   if(enrichr_available){
    #     seu <- enriched_seu
    #   }
    # }

    # annotate low read count category in seurat metadata
    seu <- add_read_count_col(seu)

    # annotate cell cycle scoring to seurat objects
    seu <- annotate_cell_cycle(seu, organism = organism, ...)

    # annotate mitochondrial percentage in seurat metadata
    seu <- add_percent_mito(seu, organism = organism)

    return(seu)
}