make_se_test: Make SummarizedExperiment test data

In jmw86069/jamses: Jam SummarizedExperiment Stats

Make SummarizedExperiment test data

Description

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
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.
mreps	integer number of replicates per group, can be used to provide the number of replicates for each group in order.

Value

SummarizedExperiment object

See Also

Other jamses SE utilities: se_collapse_by_column(), se_collapse_by_row(), se_detected_rows(), se_normalize(), se_rbind(), se_to_rowcoldata()

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")
}

# 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
# use naControlFloor
hm2c <- heatmap_se(se2,
   column_title="centered vs A\nnaControlFloor=7",
   naControlFloor=7, naControlAction="floor", controlSamples=1:3)
hm2a + hm2b + hm2c

jmw86069/jamses documentation built on Nov. 4, 2024, 9:25 p.m.