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Plot run QC

Source: R/plotting_functions.R
plot_run_qc.Rd

plot_run_qc() creates a visualization of the outcome of the QC step. The top panel depicts the outcome of the cellwise QC. The various cellwise QC filters (e.g., "N nonzero responses," "N response UMIs," "Percent mito", etc.) are shown on the horizontal axis, and the percentage of cells removed due application of a given QC filter is shown on the vertical axis. Note that a cell can be flagged by multiple QC filters; for example, a cell might have an extremely high response_n_umi value and an extremely high response_n_nonzero value. Thus, the height of the "any filter" bar (which indicates the percentage of cells removed due to application of any filter) need not be equal to the sum of the heights of the other bars. The bottom panel depicts the outcome of the pairwise QC. Each point corresponds to a target-response pair; the vertical axis (resp., horizontal axis) indicates the n_nonzero_trt (resp., n_nonzero_cntrl) value of that pair. Pairs for which n_nonzero_trt or n_nonzero_cntrl fall below the threshold are removed (red), while the remaining pairs are retained (green).

Usage

plot_run_qc(
  sceptre_object,
  downsample_pairs = 10000L,
  point_size = 0.55,
  transparency = 0.8,
  return_indiv_plots = FALSE
)

Arguments

sceptre_object

a sceptre_object that has had run_qc() called on it

downsample_pairs

(optional; default 10000) the maximum number of points to plot in the lower panel of the figure (i.e., the "pairwise QC" plot)

point_size

(optional; default 0.55) the size of the individual points in the plot

transparency

(optional; default 0.8) the transparency of the individual points in the plot

return_indiv_plots

(optional; default FALSE) if FALSE then a list of ggplot is returned; if TRUE then a single cowplot object is returned.

Value

a single cowplot object containing the combined panels (if return_indiv_plots is set to TRUE) or a list of the individual panels (if return_indiv_plots is set to FALSE)

Examples

library(sceptredata)
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
)
discovery_pairs <- construct_cis_pairs(sceptre_object)
sceptre_object |>
  set_analysis_parameters(
    discovery_pairs = discovery_pairs,
    side = "left"
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc() |>
  plot_run_qc()