Skip to contents

Run QC

Source: R/s4_analysis_functs_1.R
run_qc.Rd

run_qc() runs cellwise and pairwise QC on the data. Cellwise QC involves filtering cells on the covariates response_n_nonzero, response_n_umis, and response_p_mito. In low-MOI we additionally remove cells that contain zero or multiple gRNAs. Next, pairwise QC involves filtering out target-response pairs whose data are too sparse to be analyzed reliably. In this context we define the “number of nonzero treatment cells” (resp., the “number of nonzero control cells”) as the number of cells in the treatment group (resp., control group) that contain nonzero expression of the response. (We sometimes use the shorthand n_nonzero_trt and n_nonzero_cntrl to refer to the number of nonzero treatment cells and control cells, respectively.) Pairwise QC involves filtering target-response pairs on n_nonzero_trt and n_nonzero_cntrl. See Chapter 4 of the manual for more detailed information about this function.

Usage

run_qc(
  sceptre_object,
  n_nonzero_trt_thresh = 7L,
  n_nonzero_cntrl_thresh = 7L,
  response_n_umis_range = c(0.01, 0.99),
  response_n_nonzero_range = c(0.01, 0.99),
  p_mito_threshold = 0.2,
  additional_cells_to_remove = integer()
)

Arguments

sceptre_object

a sceptre_object

n_nonzero_trt_thresh

(optional; default 7L) an integer specifying the number of nonzero treatment cells a pair must contain for it to be retained

n_nonzero_cntrl_thresh

(optional; default 7L) an integer specifying the number of nonzero control cells a pair must contain for it to be retained

response_n_umis_range

(optional; default c(0.01, 0.99)) a length-two vector of percentiles specifying the location at which to clip the left and right tails of the response_n_umis distribution

response_n_nonzero_range

(optional; default c(0.01, 0.99)) a length-two vector of percentiles specifying the location at which to clip the left and right tails of the response_n_nonzero distribution

p_mito_threshold

(optional; default 0.2) a numeric value specifying the location at which to clip the right tail of the response_p_mito distribution

additional_cells_to_remove

(optional) a vector of integer indices specifying additional cells to remove

Value

an updated sceptre_object in which cellwise and pairwise QC have been applied

Examples

data(highmoi_example_data)
data(grna_target_data_frame_highmoi)
# import data
sceptre_object <- import_data(
  response_matrix = highmoi_example_data$response_matrix,
  grna_matrix = highmoi_example_data$grna_matrix,
  grna_target_data_frame = grna_target_data_frame_highmoi,
  moi = "high",
  extra_covariates = highmoi_example_data$extra_covariates,
  response_names = highmoi_example_data$gene_names
)

# set analysis parameters, assign grnas, run qc
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 <- sceptre_object |>
  set_analysis_parameters(
    discovery_pairs = discovery_pairs,
    positive_control_pairs = positive_control_pairs,
    side = "left"
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc()