R/sceptre.R

In timothy-barry/sceptre: Analysis of Single-Cell CRISPR Screen Data

utils::globalVariables(c(
  "n_nonzero_trt", "n_nonzero_cntrl", "pair_str", "assignment", "g",
  "multiple_grnas", "x", "grna_id", "grna_expressions_bin", "bin_counts",
  "significant", "lab", "p_values", "pass_qc", "fraction_cells_removed",
  "grna_id", "pass_qc", "grna_group", "p_value", "gene_id", "response_id",
  "log_2_fold_change", "reject", "y", "grna_target", "gene_position_data_frame_grch38",
  "any_pass_qc", "vector_id", "bin", "normalized_count", "is_zero", "treatment",
  "n_distinct_targets"
))
#' sceptre
#'
#' `sceptre` is an R package for single-cell CRISPR screen data analysis, emphasizing statistical rigor, massive scalability, and ease of use.
#'
#' @useDynLib sceptre, .registration = TRUE
#' @importFrom Rcpp sourceCpp
#' @import BH
#' @keywords package
#' @examples
#' ##########################
#' # Low-MOI CRISPRko example
#' ##########################
#' library(sceptredata)
#' # 1. create the sceptre object
#' data("lowmoi_example_data")
#' sceptre_object <- import_data(
#'   response_matrix = lowmoi_example_data$response_matrix,
#'   grna_matrix = lowmoi_example_data$grna_matrix,
#'   extra_covariates = lowmoi_example_data$extra_covariates,
#'   grna_target_data_frame = lowmoi_example_data$grna_target_data_frame,
#'   moi = "low"
#' )
#' print(sceptre_object)
#'
#' # 2. set the analysis parameters
#' positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
#' discovery_pairs <- construct_trans_pairs(
#'   sceptre_object = sceptre_object,
#'   positive_control_pairs = positive_control_pairs,
#'   pairs_to_exclude = "pc_pairs"
#' )
#'
#' sceptre_object <- set_analysis_parameters(
#'   sceptre_object = sceptre_object,
#'   discovery_pairs = discovery_pairs,
#'   positive_control_pairs = positive_control_pairs
#' )
#' print(sceptre_object)
#'
#' # 3. assign grnas
#' plot_grna_count_distributions(sceptre_object)
#' sceptre_object <- sceptre_object |> assign_grnas()
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 4. run qc
#' plot_covariates(sceptre_object, p_mito_threshold = 0.075)
#' sceptre_object <- sceptre_object |> run_qc(p_mito_threshold = 0.075)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 5. run the calibration check
#' sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 6. run power check
#' sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 7. run discovery analysis
#' sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 8. write results
#' write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_lowmoi/")
#'
#' ##########################
#' # High-MOI CRISPRi example
#' ##########################
#' # 1. create the sceptre object from cellranger output
#' directories <- paste0(
#'   system.file("extdata", package = "sceptredata"),
#'   "/highmoi_example/gem_group_", c(1, 2)
#' )
#' data(grna_target_data_frame_highmoi)
#' sceptre_object <- import_data_from_cellranger(
#'   directories = directories,
#'   moi = "high",
#'   grna_target_data_frame = grna_target_data_frame_highmoi
#' )
#' print(sceptre_object)
#'
#' # 2. set the analysis parameters
#' positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
#' discovery_pairs <- construct_cis_pairs(sceptre_object,
#'   positive_control_pairs = positive_control_pairs,
#'   distance_threshold = 5e6
#' )
#'
#' sceptre_object <- set_analysis_parameters(
#'   sceptre_object = sceptre_object,
#'   discovery_pairs = discovery_pairs,
#'   positive_control_pairs = positive_control_pairs,
#'   side = "left"
#' )
#' print(sceptre_object)
#'
#' # 3. assign grnas
#' plot_grna_count_distributions(sceptre_object)
#' sceptre_object <- sceptre_object |> assign_grnas(parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 4. run qc
#' plot_covariates(sceptre_object, p_mito_threshold = 0.075)
#' sceptre_object <- sceptre_object |> run_qc(p_mito_threshold = 0.075)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 5. run the calibration check
#' sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 6. run the power check
#' sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 7. run discovery analysis
#' sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
#' plot(sceptre_object)
#' print(sceptre_object)
#'
#' # 8. write results
#' write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_highmoi/")
"_PACKAGE"