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scTenifoldNet Plot

Source: R/scTenifoldKnkPlot.R
scTenifoldNetPlot.Rd

Visualize condition-level network comparison results generated by RunscTenifoldNet.

Usage

scTenifoldNetPlot(
  object,
  tool_name = "scTenifoldNet",
  plot_type = c("qq", "effect", "network", "manifold"),
  network = c("X", "Y"),
  top_n = 20,
  FDR_threshold = 0.05,
  FC_threshold = 1,
  features = NULL,
  label = TRUE,
  nlabel = 10,
  pt.size = 2,
  pt.alpha = 0.85,
  label.size = 3.5,
  edge_top_n = 100,
  edge_threshold = NULL,
  arrow.length = 0.14,
  arrow.linewidth = 0.8,
  arrow.alpha = 0.8,
  start.pt.size = 2.2,
  end.pt.size = 3,
  manifold.aspect.ratio = 1,
  cols.sig = "#D7301F",
  cols.ns = "grey70",
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  theme_use = "theme_scop",
  theme_args = list(),
  verbose = TRUE,
  srt = NULL
)

Arguments

object

A Seurat object containing a scTenifoldNet result in srt@tools.

tool_name

Name of the srt@tools entry created by RunscTenifoldNet.

plot_type

Plot type. "qq" shows the common FC versus theoretical chi-square quantile plot. "effect" ranks top differential-regulation genes by FC. "network" draws a condition-specific tensor network subgraph from saved tensor networks using thisplot::GraphPlot. "manifold" draws paired condition displacement arrows in the non-linear manifold-alignment space.

network

Tensor network to show for plot_type = "network". Upstream scTenifoldNet results usually contain "X" and "Y" networks.

top_n

Number of top genes to show or label.

FDR_threshold

Adjusted p-value cutoff used to mark significant genes.

FC_threshold

FC cutoff used to mark significant genes.

features

Character vector of genes to show or label. For plot_type = "network" and "manifold", these genes are included in the displayed subset when present.

label

Whether to label genes.

nlabel

Number of labels to add when features = NULL.

pt.size

Point size.

pt.alpha

Point transparency.

label.size

Label text size.

edge_top_n

Maximum number of network edges to draw.

edge_threshold

Minimum absolute edge weight for plot_type = "network". If NULL, the largest edge_top_n edges are used.

arrow.length

Arrow head length for plot_type = "manifold".

arrow.linewidth

Arrow line width for plot_type = "manifold".

arrow.alpha

Arrow transparency for plot_type = "manifold".

start.pt.size

WT-position point size for plot_type = "manifold".

end.pt.size

KO-position point size for plot_type = "manifold".

manifold.aspect.ratio

Panel aspect ratio for plot_type = "manifold". Default 1 keeps the panel square. Set NULL to use the device aspect ratio.

cols.sig

Color for significant genes.

cols.ns

Color for non-significant genes.

xlab, ylab

Axis labels.

title

Plot title. If NULL, a default title is used.

theme_use

Theme function used to style the plot.

theme_args

Other arguments passed to theme_use.

verbose

Whether to print the message. Default is TRUE.

srt

Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

A ggplot object.

Examples

data(pancreas_sub)
counts <- GetAssayData5(pancreas_sub, assay = "RNA", layer = "counts")
detected <- names(sort(Matrix::rowSums(counts > 0), decreasing = TRUE))
features_use <- head(detected, 300)

pancreas_sub <- RunscTenifoldNet(
  pancreas_sub,
  group.by = "CellType",
  condition1 = "Ductal",
  condition2 = "Endocrine",
  features = features_use,
  qc = FALSE,
  nc_nNet = 3,
  nc_nCells = 200,
  td_maxIter = 200,
  ma_nDim = 2,
  store_networks = TRUE,
  store_manifold = TRUE
)
#> ℹ [2026-11-12 06:13:36] Run scTenifoldNet comparison using upstream implementation
#> 
#> ── scTenifoldNet Pipeline ──────────────────────────────────────────────────────
#> ℹ Step 2/6: CPM normalization
#> ℹ [X] CPM normalization: 300 genes x 253 cells
#> ✔ [X] CPM normalization complete
#> ℹ [Y] CPM normalization: 300 genes x 355 cells
#> ✔ [Y] CPM normalization complete
#> ℹ Shared genes: 300
#> ℹ Step 3/6: Building gene regulatory networks
#> ℹ [X] Building 3 gene regulatory networks (200 cells each)
#> ✔ [X] Network construction complete: 3 networks
#> ℹ [Y] Building 3 gene regulatory networks (200 cells each)
#> ✔ [Y] Network construction complete: 3 networks
#> ℹ Step 4/6: Tensor decomposition
#> ℹ [X] Tensor: 300 x 300 x 3 (K=3)
#> ✔ [X] CP decomposition complete (norm explained: 26.3%)
#> ℹ [Y] Tensor: 300 x 300 x 3 (K=3)
#> ✔ [Y] CP decomposition complete (norm explained: 24.7%)
#> ℹ Step 5/6: Manifold alignment
#> ℹ Manifold alignment: 300 shared genes, d=2
#> ✔ Manifold alignment complete: 2 dimensions
#> ℹ Step 6/6: Differential regulation analysis
#> ℹ Computing distances for 300 genes
#> ✔ Differential regulation complete: 2/300 significant genes (FDR < 0.05)
#> ✔ scTenifoldNet pipeline complete
#> ✔ [2026-11-12 06:13:36] scTenifoldNet results stored in `object@tools[[scTenifoldNet]]`

scTenifoldNetPlot(pancreas_sub, plot_type = "effect")


scTenifoldNetPlot(pancreas_sub, plot_type = "network", network = "X")


scTenifoldNetPlot(pancreas_sub, plot_type = "manifold")