extract_modules: Extract modules of features

In SCORPIUS: Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data

Extract modules of features

Description

extract_modules uses adaptive branch pruning to extract modules of features, which is typically done on the smoothed expression returned by gene_importances.

Usage

extract_modules(
  x,
  time = NULL,
  suppress_warnings = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x	A numeric matrix or a data frame with M rows (one per sample) and P columns (one per feature).
time	(Optional) Order the modules according to a pseudotime
suppress_warnings	Whether or not to suppress warnings when P > 1000
verbose	Whether or not Mclust will print output or not
...	Extra parameters passed to Mclust

Value

A data frame containing meta-data for the features in x, namely the order in which to visualise the features in and which module they belong to.

See Also

gene_importances

Examples

## Generate a dataset and visualise
dataset <- generate_dataset(num_genes=300, num_samples=200, num_groups=4)
expression <- dataset$expression
group_name <- dataset$sample_info$group_name
space <- reduce_dimensionality(expression, ndim=2)
traj <- infer_trajectory(space)
time <- traj$time
draw_trajectory_plot(space, path=traj$path, group_name)

## Select most important genes (set ntree to at least 10000!)
gimp <- gene_importances(expression, traj$time, num_permutations = 0, ntree = 1000)
gene_sel <- gimp[1:50,]
expr_sel <- expression[,gene_sel$gene]

## Group the genes into modules and visualise the modules in a heatmap
modules <- extract_modules(scale_quantile(expr_sel))
draw_trajectory_heatmap(expr_sel, time, group_name, modules)

SCORPIUS documentation built on Aug. 8, 2023, 1:09 a.m.