Make SummarizedExperiment test data

Usage

make_se_test(
  ngroups = 2,
  nreps = 3,
  nrow = 50,
  multiplier = 1,
  offset = 7,
  hit_fraction = 1/2,
  hit_max = 2.8,
  noise_factor = 1,
  seed = 123,
  assay_name = "counts",
  sparsity = 0,
  verbose = FALSE,
  ...
)

Arguments

ngroups: integer number of experimental groups
nreps: integer number of replicates per group, can be used to provide the number of replicates for each group in order.
nrow: integer number of rows (measurements)
multiplier: numeric value multiplied by rnorm() to adjust the magnitude of values produced, default 1.
offset: numeric value added to the output of rnorm(), default 7.
hit_fraction: numeric value between 0 and 1 indicating the fraction of rows to simulate as having a fold change, default 1/2.
hit_max: numeric maximum value for a simulated fold change, default 2.8, intended to be interpreted as log2 of a 7-fold change.
noise_factor: numeric multiplied by rnorm() to add additional noise.
seed: numeric passed to set.seed() when provided, for reproducible random output.
assay_name: character name to use for the assay name in the output.
sparsity: numeric value from 0 to 1, default 0, indicating the fraction of values that are converted to NA to simulate sparse data measurements. It can be provided as a vector and applied to each group in order. In some proteomics datasets, control samples may be substantially more sparse than other groups, for example if the control is non-targeted IP, or negative control. In this case, data can be simulated by using sparsity=c(0.6, 0, 0) for three groups.
verbose: logical indicating whether to print verbose output.
...: additional arguments are ignored.

Value

SummarizedExperiment object

Examples

se <- make_se_test();
heatmap_se(se)

heatmap_se(se, controlSamples=1:3)


se <- make_se_test(ngroups=3, assay_name="expression");
heatmap_se(se, apply_hm_column_title=TRUE)

heatmap_se(se, controlSamples=1:3, control_label="versus A", apply_hm_column_title=TRUE)


se <- make_se_test(ngroups=3, assay_name="expression", nreps=c(3, 5, 4));
heatmap_se(se, controlSamples=1:3, control_label="versus A", apply_hm_column_title=TRUE)


se <- make_se_test(ngroups=3, assay_name="expression", nreps=c(3, 5, 4), hit_max=4);
heatmap_se(se, controlSamples=1:3, control_label="versus A", apply_hm_column_title=TRUE)


sedesign <- groups_to_sedesign(SummarizedExperiment::colData(se)[, "group", drop=FALSE])
plot_sedesign(sedesign)

sestats <- se_contrast_stats(se=se, sedesign=sedesign, assay_names="expression")
plot_sedesign(sedesign, sestats=sestats,
   contrast_style="none", sestats_style="label")

heatmap_se(se, controlSamples=1:3, control_label="versus A",
   column_split="group",
   apply_hm_column_title=TRUE, sestats=sestats, color_max=5)


if (jamba::check_pkg_installed("venndir")) {
   venndir::venndir(hit_array_to_list(sestats), overlap_type="each")
   venndir::venndir(hit_array_to_list(sestats), overlap_type="each", proportional=TRUE)
   venndir::venndir(hit_array_to_list(sestats), overlap_type="each", show_labels="ncs")
}




# data.frame summary
sestats_to_dfs(sestats)
#> Error in sestats_to_dfs(sestats): could not find function "sestats_to_dfs"

# demonstrate sparsity
se2 <- make_se_test(sparsity=c(0.5, 0));
hm2a <- heatmap_se(se2,
   column_title="global centered\nall values shown")
# when centering versus a sparse control group, some values can be lost:
hm2b <- heatmap_se(se2, controlSamples=1:3,
   column_title="centered vs A\nsome values become NA")
hm2a + hm2b
#> Warning: Heatmap/annotation names are duplicated: centered expression

# use naControlFloor
hm2c <- heatmap_se(se2,
   column_title="centered vs A\nnaControlFloor=7",
   naControlFloor=7, naControlAction="floor", controlSamples=1:3)
hm2a + hm2b + hm2c
#> Warning: Heatmap/annotation names are duplicated: centered expression
#> Warning: Heatmap/annotation names are duplicated: centered expression, centered
#> expression

Make SummarizedExperiment test data

Usage

Arguments

Value

See also

Examples