Compute metrics.

sim_metrics computes metrics.

sim_metrics(
  collated_sim,
  sim_type_background,
  calculate_grouped = FALSE,
  annotation_prefix = "Metadata_",
  use_furrr = FALSE,
  calculate_pvalue = FALSE,
  ...
)

Arguments

collated_sim

output of sim_collated, which is a data.frame with some attributes.

sim_type_background

character string specifying the background distributions for computing scaled metrics. This must be one of the strings "non_rep" or "ref".

calculate_grouped

optional boolean specifying whether to include grouped metrics.

annotation_prefix

optional character string specifying prefix for annotation columns (e.g. "Metadata_" (default)).

use_furrr

boolean indicating whether to use the furrr library for parallel processing.

calculate_pvalue

optional boolean specifying whether to calculate p-values for the metrics

...

arguments passed down to "sim_metrics_signif"

Value

List of metrics.

Examples

suppressMessages(suppressWarnings(library(ggplot2)))

cellhealth_subset <-
  matric::cellhealth %>%
  dplyr::filter(Metadata_cell_line == "A549") %>%
  dplyr::group_by(Metadata_cell_line,
                  Metadata_gene_name,
                  Metadata_pert_name) %>%
  dplyr::slice_sample(n = 3) %>%
  dplyr::ungroup()

sim_df <- matric::sim_calculate(cellhealth_subset)

drop_group <-
  data.frame(Metadata_gene_name = "EMPTY")

reference <-
  data.frame(Metadata_gene_name = c("Chr2"))

all_same_cols_ref <-
  c(
    "Metadata_cell_line",
    "Metadata_Plate"
  )

all_same_cols_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

all_same_cols_rep_ref <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name",
    "Metadata_Plate"
  )

any_different_cols_non_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

all_same_cols_non_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_Plate"
  )

all_different_cols_non_rep <-
  c("Metadata_gene_name")

all_same_cols_group <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name"
  )

any_different_cols_group <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

annotation_cols <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

collated_sim <-
  matric::sim_collate(
    sim_df,
    reference = reference,
    all_same_cols_rep = all_same_cols_rep,
    all_same_cols_rep_ref = all_same_cols_rep_ref,
    all_same_cols_ref = all_same_cols_ref,
    any_different_cols_non_rep = any_different_cols_non_rep,
    all_same_cols_non_rep = all_same_cols_non_rep,
    all_different_cols_non_rep = all_different_cols_non_rep,
    any_different_cols_group = any_different_cols_group,
    all_same_cols_group = all_same_cols_group,
    annotation_cols = annotation_cols,
    drop_group = drop_group
  )

metrics <- matric::sim_metrics(collated_sim, "ref", calculate_grouped = TRUE)

ggplot(
  metrics$level_1_0,
  aes(sim_scaled_mean_ref_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line)


ggplot(
  metrics$level_1,
  aes(sim_scaled_mean_ref_i_mean_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line)


ggplot(
  metrics$level_2_1,
  aes(sim_scaled_mean_ref_g, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line)