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SCORPIUS an unsupervised approach for inferring linear developmental chronologies from single-cell RNA sequencing data. In comparison to similar approaches, it has three main advantages:
It accurately reconstructs linear dynamic processes. The performance was evaluated using a quantitative evaluation pipeline and ten single-cell RNA sequencing datasets.
It automatically identifies marker genes, speeding up knowledge discovery.
It is fully unsupervised. Prior knowledge of the relevant marker genes or cellular states of individual cells is not required.
News:
See news(package = "SCORPIUS")
for a full list of
changes to the package.
Our preprint is on bioRxiv (Cannoodt et al. 2016).
Check out our review on Trajectory Inference methods (Saelens et al. 2019).
You can install:
the latest released version from CRAN with
install.packages("SCORPIUS")
the latest development version from GitHub with
::install_github("rcannood/SCORPIUS", build_vignettes = TRUE) devtools
If you encounter a bug, please file a minimal reproducible example on the issues page.
To get started, read the introductory example below, or read one of the vignettes containing more elaborate examples:
vignette("anndata", package="SCORPIUS")
vignette("ginhoux", package="SCORPIUS")
vignette("seurat", package="SCORPIUS")
vignette("simulated-data", package="SCORPIUS")
This section describes the main workflow of SCORPIUS without going in depth in the R code. For a more detailed explanation, see the vignettes listed below.
To start using SCORPIUS, simply write:
library(SCORPIUS)
The ginhoux
dataset (Schlitzer et al. 2015) contains 248
dendritic cell progenitors in one of three cellular cellular states:
MDP, CDP or PreDC. Note that this is a reduced version of the dataset,
for packaging reasons. See ?ginhoux for more info.
data(ginhoux)
<- ginhoux$expression
expression <- ginhoux$sample_info$group_name group_name
With the following code, SCORPIUS reduces the dimensionality of the dataset and provides a visual overview of the dataset. In this plot, cells that are similar in terms of expression values will be placed closer together than cells with dissimilar expression values.
<- reduce_dimensionality(expression, "spearman")
space draw_trajectory_plot(space, group_name, contour = TRUE)
To infer and visualise a trajectory through these cells, run:
<- infer_trajectory(space)
traj draw_trajectory_plot(space, group_name, traj$path, contour = TRUE)
To identify candidate marker genes, run:
# warning: setting num_permutations to 10 requires a long time (~30min) to run!
# set it to 0 and define a manual cutoff for the genes (e.g. top 200) for a much shorter execution time.
<- gene_importances(
gimp
expression, $time,
trajnum_permutations = 10,
num_threads = 8,
ntree = 10000,
ntree_perm = 1000
)
To select the most important genes and scale its expression, run:
$qvalue <- p.adjust(gimp$pvalue, "BH", length(gimp$pvalue))
gimp<- gimp$gene[gimp$qvalue < .05]
gene_sel <- scale_quantile(expression[,gene_sel]) expr_sel
To visualise the expression of the selected genes, use the
draw_trajectory_heatmap
function.
draw_trajectory_heatmap(expr_sel, traj$time, group_name)
Finally, these genes can also be grouped into modules as follows:
<- extract_modules(scale_quantile(expr_sel), traj$time, verbose = F)
modules draw_trajectory_heatmap(expr_sel, traj$time, group_name, modules)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.