Type:	Package
Title:	Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data
Version:	1.0.9
Description:	An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, 'SCORPIUS' provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) <doi:10.1101/079509>.
License:	GPL-3
Encoding:	UTF-8
URL:	https://github.com/rcannood/SCORPIUS, http://rcannood.github.io/SCORPIUS/
BugReports:	https://github.com/rcannood/SCORPIUS/issues
LazyData:	true
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
Depends:	R (≥ 3.5.0)
Imports:	dplyr, dynutils (≥ 1.0.3), dynwrap, grDevices, ggplot2 (≥ 2.0), lmds, MASS, Matrix, mclust, methods, pbapply, pheatmap, princurve (≥ 2.1.4), purrr, ranger, RANN, RColorBrewer, reshape2, stats, tidyr, TSP
Suggests:	anndata, covr, knitr, reticulate, rmarkdown, Seurat, SingleCellExperiment, testthat (≥ 2.1.0)
NeedsCompilation:	no
Packaged:	2023-07-21 19:26:06 UTC; rcannood
Author:	Robrecht Cannoodt [aut, cre] (github: rcannood), Wouter Saelens [ctb] (github: zouter)
Maintainer:	Robrecht Cannoodt <rcannood@gmail.com>
Repository:	CRAN
Date/Publication:	2023-08-07 17:30:05 UTC

SCORPIUS: Trajectory inference from single-cell RNA sequencing data.

Description

SCORPIUS orders single cells with regard to an implicit timeline, such as cellular development or progression over time.

Dimensionality Reduction functions

reduce_dimensionality

Trajectory Inference functions

infer_trajectory, infer_initial_trajectory, reverse_trajectory, gene_importances, extract_modules

Visualisation functions

draw_trajectory_plot, draw_trajectory_heatmap

Datasets

generate_dataset, ginhoux

References

Cannoodt R. et al., SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development, bioRxiv (Oct., 2016). doi:10.1101/079509 (PDF).

Examples

## Load dataset from Schlitzer et al., 2015
data("ginhoux")

## Reduce dimensionality and infer trajectory with SCORPIUS
space <- reduce_dimensionality(ginhoux$expression, "spearman")
traj <- infer_trajectory(space)

## Visualise
draw_trajectory_plot(
  space,
  path = traj$path,
  progression_group = ginhoux$sample_info$group_name
)

Draw time-series heatmap

Description

draw_trajectory_heatmap draws a heatmap in which the samples are ranked according their position in an inferred trajectory. In addition, the progression groups and feature modules can be passed along to further enhance the visualisation.

Usage

draw_trajectory_heatmap(
  x,
  time,
  progression_group = NULL,
  modules = NULL,
  show_labels_row = FALSE,
  show_labels_col = FALSE,
  scale_features = TRUE,
  progression_group_palette = NULL,
  ...
)

Arguments

Value

The output of the pheatmap function.

Examples

## Generate a dataset
dataset <- generate_dataset(num_genes=500, num_samples=300, num_groups=4)
expression <- dataset$expression
space <- reduce_dimensionality(expression, ndim=2)
groups <- dataset$sample_info$group_name
traj <- infer_trajectory(space)
time <- traj$time

gimp <- gene_importances(expression, traj$time, num_permutations = 0, ntree = 10000)
gene_sel <- gimp[1:50,]
expr_sel <- expression[,gene_sel$gene]

## Draw a time series heatmap
draw_trajectory_heatmap(expr_sel, time)

## Also show the progression groupings
draw_trajectory_heatmap(expr_sel, time, progression_group=groups)

## Use a different palette
draw_trajectory_heatmap(
  expr_sel, time, progression_group=groups,
  progression_group_palette = setNames(RColorBrewer::brewer.pal(4, "Set2"), paste0("Group ", 1:4))
)

## 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, progression_group=groups, modules=modules)

Visualise SCORPIUS

Description

draw_trajectory_plot is used to plot samples after performing dimensionality reduction. Additional arguments can be provided to colour the samples, plot the trajectory inferred by SCORPIUS, and draw a contour around the samples.

Usage

draw_trajectory_plot(
  space,
  progression_group = NULL,
  path = NULL,
  contour = FALSE,
  progression_group_palette = NULL,
  point_size = 2,
  point_alpha = 1,
  path_size = 0.5,
  path_alpha = 1,
  contour_alpha = 0.2
)

Arguments

Value

A ggplot2 plot.

Examples

## Generate a synthetic dataset
dataset <- generate_dataset(num_genes = 500, num_samples = 300, num_groups = 4)
space <- reduce_dimensionality(dataset$expression, ndim = 2)
groups <- dataset$sample_info$group_name

## Simply plot the samples
draw_trajectory_plot(space)

## Colour each sample according to its group
draw_trajectory_plot(space, progression_group = groups)

## Add contours to the plot
draw_trajectory_plot(space, progression_group = groups, contour = TRUE)

## Plot contours without colours
draw_trajectory_plot(space, contour = TRUE)

## Infer a trajectory and plot it
traj <- infer_trajectory(space)
draw_trajectory_plot(space, progression_group = groups, path = traj$path)
draw_trajectory_plot(space, progression_group = groups, path = traj$path, contour = TRUE)

## Visualise gene expression
draw_trajectory_plot(space, progression_group = dataset$expression[,1])

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

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)

Calculate the importance of a feature

Description

Calculates the feature importance of each column in x in trying to predict the time ordering.

Usage

gene_importances(
  x,
  time,
  num_permutations = 0,
  ntree = 10000,
  ntree_perm = ntree/10,
  mtry = ncol(x) * 0.01,
  num_threads = 1,
  ...
)

Arguments

Value

a data frame containing the importance of each feature for the given time line

Examples

dataset <- generate_dataset(num_genes=500, num_samples=300, num_groups=4)
expression <- dataset$expression
group_name <- dataset$sample_info$group_name
space <- reduce_dimensionality(expression, ndim=2)
traj <- infer_trajectory(space)
# set ntree to at least 1000!
gene_importances(expression, traj$time, num_permutations = 0, ntree = 1000)

Generate a synthetic dataset

Description

generate_dataset generates an synthetic dataset which can be used for visualisation purposes.

Usage

generate_dataset(
  num_samples = 400,
  num_genes = 500,
  num_groups = 4
)

Arguments

Value

A list containing the expression data and the meta data of the samples.

See Also

SCORPIUS

Examples

## Generate a dataset
dataset <- generate_dataset(num_genes = 200, num_samples = 400, num_groups = 4)

## Reduce dimensionality and infer trajectory with SCORPIUS
space <- reduce_dimensionality(dataset$expression, ndim = 2)
traj <- infer_trajectory(space)

## Visualise
draw_trajectory_plot(space, path=traj$path, progression_group=dataset$sample_info$group_name)

scRNA-seq data of dendritic cell progenitors.

Description

This dataset contains the expression values of the top 2000 most variable genes for 248 dendritic cell progenitors. Each cell is in one of three maturation stages: MDP, CDP or PreDC. The levels of the factor in sample.info are ordered according to the maturation process.

The number of genes had to be reduced specifically for reducing the package size of SCORPIUS. Use the following code to download the original data:

download.file("https://github.com/rcannood/SCORPIUS/raw/master/data-raw/ginhoux_orig.rds", destfile = "local.rds")
ginhoux <- readRDS("local.rds")
# do something with ginhoux

Usage

ginhoux

Format

A list containing two data frames, expression (248x2000) and sample_info (248x1).

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60783

References

Schlitzer A, Sivakamasundari V, Chen J, Sumatoh HR et al. Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat Immunol 2015 Jul;16(7):718-28. PMID: 26054720

See Also

SCORPIUS

Infer an initial trajectory through space

Description

infer_initial_trajectory infers an initial trajectory for infer_trajectory by clustering the points and calculating the shortest path through cluster centers. The shortest path takes into account the euclidean distance between cluster centers, and the density between those two points.

Usage

infer_initial_trajectory(space, k)

Arguments

Value

the initial trajectory obtained by this method

Infer linear trajectory through space

Description

infer_trajectory infers a trajectory through samples in a given space in a four-step process:

1. Perform k-means clustering

2. Calculate distance matrix between cluster centers using a custom distance function

3. Find the shortest path connecting all cluster centers using the custom distance matrix

4. Iteratively fit a curve to the given data using principal curves

Usage

infer_trajectory(
  space,
  k = 4,
  thresh = 0.001,
  maxit = 10,
  stretch = 0,
  smoother = "smooth_spline",
  approx_points = 100
)

Arguments

Value

A list containing several objects:

• path: the trajectory obtained by principal curves.

• time: the time point of each sample along the inferred trajectory.

See Also

reduce_dimensionality, draw_trajectory_plot

Examples

## Generate an example dataset and visualise it
dataset <- generate_dataset(num_genes = 200, num_samples = 400, num_groups = 4)
space <- reduce_dimensionality(dataset$expression, ndim = 2)
draw_trajectory_plot(space, progression_group = dataset$sample_info$group_name)

## Infer a trajectory through this space
traj <- infer_trajectory(space)

## Visualise the trajectory
draw_trajectory_plot(space, path=traj$path, progression_group = dataset$sample_info$group_name)

Dimensionality reduction

Description

reduce_dimensionality performs an eigenanalysis of the given dissimilarity matrix and returns coordinates of the samples represented in an ndim-dimensional space.

Usage

reduce_dimensionality(
  x,
  dist = c("spearman", "pearson", "euclidean", "cosine", "manhattan"),
  ndim = 3,
  num_landmarks = 1000
)

Arguments

Value

A matrix containing the coordinates of each sample, represented in an ndim-dimensional space.

See Also

SCORPIUS

Examples

## Generate an example dataset
dataset <- generate_dataset(num_genes = 200, num_samples = 400, num_groups = 4)

## Reduce the dimensionality of this dataset
space <- reduce_dimensionality(dataset$expression, ndim = 2)

## Visualise the dataset
draw_trajectory_plot(space, progression_group = dataset$sample_info$group_name)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dynutils

apply_minmax_scale, apply_quantile_scale, apply_uniform_scale, scale_minmax, scale_quantile, scale_uniform

Reverse a trajectory

Description

Since the direction of the trajectory is not specified, the ordering of a trajectory may be inverted using reverse_trajectory.

Usage

reverse_trajectory(trajectory)

Arguments

Value

The same trajectory, but in the other direction.

See Also

infer_trajectory

Examples

## Generate an example dataset and infer a trajectory through it
dataset <- generate_dataset(num_genes = 200, num_samples = 400, num_groups = 4)
group_name <- dataset$sample_info$group_name
space <- reduce_dimensionality(dataset$expression, ndim = 2)
traj <- infer_trajectory(space)

## Visualise the trajectory
draw_trajectory_plot(space, group_name, path = traj$path)

## Reverse the trajectory
reverse_traj <- reverse_trajectory(traj)
draw_trajectory_plot(space, group_name, path = reverse_traj$path)

plot(traj$time, reverse_traj$time, type = "l")

Infer a trajectory using SCORPIUS

Description

Pass this object to dynwrap::infer_trajectory().

Usage

ti_scorpius(
  distance_method = "spearman",
  ndim = 3L,
  k = 4L,
  thresh = 0.001,
  maxit = 10L,
  stretch = 0,
  smoother = "smooth_spline"
)

Arguments

Value

A dynwrap TI method.