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## ---- setseed, echo=FALSE-----------------------------------------------------
set.seed(1)
## ----load_data, message=FALSE-------------------------------------------------
library(SCORPIUS)
library(SingleCellExperiment)
data(ginhoux)
sce <- SingleCellExperiment(
assays = list(counts = t(ginhoux$expression)),
colData = ginhoux$sample_info
)
# short hand notation
group_name <- colData(sce)$group_name
sce
## ----perform_mds--------------------------------------------------------------
space <- reduce_dimensionality(t(assay(sce, "counts")), dist = "spearman", ndim = 3)
## ----show_dimred--------------------------------------------------------------
draw_trajectory_plot(
space,
progression_group = group_name,
contour = TRUE
)
## ----infer_trajectory---------------------------------------------------------
traj <- infer_trajectory(space)
## ----plot_trajectory----------------------------------------------------------
draw_trajectory_plot(
space,
progression_group = group_name,
path = traj$path,
contour = TRUE
)
## ----find_tafs----------------------------------------------------------------
gimp <- gene_importances(
t(assay(sce, "counts")),
traj$time,
num_permutations = 0,
num_threads = 8
)
gene_sel <- gimp[1:50,]
expr_sel <- t(assay(sce, "counts"))[,gene_sel$gene]
## ----visualise_tafs, fig.keep='first'-----------------------------------------
draw_trajectory_heatmap(expr_sel, traj$time, group_name)
## ----moduled_tafs, fig.keep='first'-------------------------------------------
modules <- extract_modules(scale_quantile(expr_sel), traj$time, verbose = FALSE)
draw_trajectory_heatmap(expr_sel, traj$time, group_name, modules)
## -----------------------------------------------------------------------------
reducedDims(sce) <- SimpleList(MDS = space)
colData(sce)$trajectory_path <- traj$path
colData(sce)$trajectory_pseudotime <- traj$time
rowData(sce)$trajectory_importance <- gimp[match(rownames(sce), gimp$gene),]$importance
sce
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