extract_modules: Extract modules of features

Description Usage Arguments Value See Also Examples

View source: R/extract_modules.R

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

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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

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## 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. 7, 2019, 5:02 p.m.