Assign gRNAs to cells — assign

assign_grnas() performs the gRNA-to-cell assignments. sceptre provides three gRNA-to-cell assignment strategies: the mixture method, the thresholding method, and the maximum method. The mixture method involves assigning gRNAs to cells using a principled mixture model. Next, the thresholding method assigns a gRNA to a cell if the UMI count of the gRNA in the cell is greater than or equal to some integer threshold. Finally, the maximum method assigns the gRNA that accounts for the greatest number of UMIs in a given cell to that cell. The maximum method is available only in low MOI. See Chapter 3 of the manual for more detailed information about assign_grnas().

Usage

assign_grnas(
  sceptre_object,
  method = "default",
  print_progress = TRUE,
  parallel = FALSE,
  n_processors = "auto",
  log_dir = tempdir(),
  ...
)

Arguments

sceptre_object: a sceptre_object
method: (optional) a string indicating the method to use to assign the gRNAs to cells, one of "mixture", "thresholding", or "maximum". The default is "maximum" in low MOI and "mixture" in high MOI.
print_progress: (optional; default TRUE) a logical indicating whether to print progress updates
parallel: (optional; default FALSE) a logical indicating whether to run the function in parallel. parallel = TRUE is recommended only on Mac; it is not supported on Windows and may behave unreliably on Linux clusters.
n_processors: (optional; default "auto") an integer specifying the number of processors to use if parallel is set to TRUE. The default, "auto", uses half the physical cores. The fraction may be tuned via the parallelly.availableCores.fraction R option.
log_dir: (optional; default tempdir()) a string indicating the directory in which to write the log files (ignored if parallel = FALSE)
...: optional method-specific additional arguments

Value

an updated sceptre_object in which the gRNA assignments have been carried out

Note

See the manual for information about the method-specific additional arguments.

Examples

data("lowmoi_example_data")
# 1. import data, set default analysis parameters
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"
) |> set_analysis_parameters()

# 2. assign gRNAs (three different methods)
sceptre_object <- sceptre_object |> assign_grnas(method = "thresholding")
sceptre_object <- sceptre_object |> assign_grnas(method = "maximum")
sceptre_object <- sceptre_object |> assign_grnas(method = "mixture")
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#> Performing gRNA-to-cell assignments for gRNA ENSG00000182704_grna1 (1 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000287679_grna1 (5 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000257275_grna2 (10 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000242110_grna1 (15 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000224311_grna2 (20 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000260303_grna1 (25 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000181374_grna2 (30 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000169836_grna1 (35 of 50)
#> Performing gRNA-to-cell assignments for gRNA ENSG00000109606_grna2 (40 of 50)
#> Performing gRNA-to-cell assignments for gRNA nt_grna5 (45 of 50)
#> Performing gRNA-to-cell assignments for gRNA nt_grna10 (50 of 50)