Heatmap plot for dynamic features along lineages

Heatmap plot for dynamic features along lineages

Usage

DynamicHeatmap(
  srt,
  lineages,
  features = NULL,
  use_fitted = FALSE,
  border = TRUE,
  heatmap_border = NULL,
  cell_annotation_border = NULL,
  feature_annotation_border = NULL,
  heatmap_border_palcolor = "black",
  cell_annotation_border_palcolor = "black",
  feature_annotation_border_palcolor = "black",
  heatmap_border_size = 1,
  cell_annotation_border_size = 1,
  feature_annotation_border_size = 1,
  flip = FALSE,
  min_expcells = 20,
  r.sq = 0.2,
  dev.expl = 0.2,
  padjust = 0.05,
  num_intersections = NULL,
  cell_density = 1,
  cell_bins = 100,
  order_by = c("peaktime", "valleytime"),
  layer = "counts",
  assay = NULL,
  exp_method = c("zscore", "raw", "fc", "log2fc", "log1p"),
  exp_legend_title = NULL,
  limits = NULL,
  lib_normalize = identical(layer, "counts"),
  libsize = NULL,
  family = NULL,
  suffix = lineages,
  n_candidates = 1000,
  minfreq = 5,
  fit_method = c("gam", "pretsa"),
  knot = 0,
  max_knot_allowed = 10,
  padjust_method = "fdr",
  cluster_features_by = NULL,
  cluster_rows = FALSE,
  cluster_row_slices = FALSE,
  cluster_columns = FALSE,
  cluster_column_slices = FALSE,
  show_row_names = FALSE,
  show_column_names = FALSE,
  row_names_side = ifelse(flip, "left", "right"),
  column_names_side = ifelse(flip, "bottom", "top"),
  row_names_rot = 0,
  column_names_rot = 90,
  row_title = NULL,
  column_title = NULL,
  row_title_side = "left",
  column_title_side = "top",
  row_title_rot = 0,
  column_title_rot = ifelse(flip, 90, 0),
  feature_split = NULL,
  feature_split_by = NULL,
  n_split = NULL,
  split_order = NULL,
  split_method = c("mfuzz", "kmeans", "kmeans-peaktime", "hclust", "hclust-peaktime"),
  decreasing = FALSE,
  fuzzification = NULL,
  anno_terms = FALSE,
  anno_keys = FALSE,
  anno_features = FALSE,
  terms_width = grid::unit(4, "in"),
  terms_stat_width = grid::unit(1.35, "in"),
  terms_fontsize = 8,
  terms_stat = "none",
  terms_stat_digits = 2,
  terms_stat_label = "value",
  terms_stat_axis = FALSE,
  terms_stat_background_palcolor = NULL,
  terms_stat_border = NULL,
  terms_stat_border_palcolor = NULL,
  terms_stat_border_size = NULL,
  terms_stat_label_palcolor = NULL,
  terms_group_background = FALSE,
  terms_background_palcolor = "grey98",
  terms_background_alpha = 1,
  terms_border = TRUE,
  terms_border_palcolor = "black",
  terms_border_size = 0.8,
  terms_text_palcolor = NULL,
  terms_bar_palcolor = NULL,
  keys_width = grid::unit(2, "in"),
  keys_fontsize = c(6, 10),
  features_width = grid::unit(2, "in"),
  features_fontsize = c(6, 10),
  IDtype = "symbol",
  species = "Homo_sapiens",
  db_update = FALSE,
  db_version = "latest",
  db_combine = FALSE,
  convert_species = FALSE,
  Ensembl_version = NULL,
  mirror = NULL,
  db = "GO_BP",
  TERM2GENE = NULL,
  TERM2NAME = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  GO_simplify = FALSE,
  GO_simplify_cutoff = "p.adjust < 0.05",
  simplify_method = "Wang",
  simplify_similarityCutoff = 0.7,
  pvalueCutoff = NULL,
  padjustCutoff = 0.05,
  topTerm = 5,
  show_termid = FALSE,
  topWord = 20,
  words_excluded = NULL,
  nlabel = 20,
  features_label = NULL,
  label_size = 10,
  label_color = "black",
  pseudotime_label = NULL,
  pseudotime_label_color = "black",
  pseudotime_label_linetype = 2,
  pseudotime_label_linewidth = 3,
  heatmap_palette = "viridis",
  heatmap_palcolor = NULL,
  pseudotime_palette = "cividis",
  pseudotime_palcolor = NULL,
  feature_split_palette = "simspec",
  feature_split_palcolor = NULL,
  cell_annotation = NULL,
  cell_annotation_palette = "Chinese",
  cell_annotation_palcolor = NULL,
  cell_annotation_params = if (flip) {
     list(width = grid::unit(5, "mm"))
 } else {
 
       list(height = grid::unit(5, "mm"))
 },
  feature_annotation = NULL,
  feature_annotation_palette = "Dark2",
  feature_annotation_palcolor = NULL,
  feature_annotation_params = if (flip) {
     list(height = grid::unit(5, "mm"))
 } else
    {
     list(width = grid::unit(5, "mm"))
 },
  separate_annotation = NULL,
  separate_annotation_palette = "Chinese",
  separate_annotation_palcolor = NULL,
  separate_annotation_params = if (flip) {
     list(width = grid::unit(10, "mm"))
 }
    else {
     list(height = grid::unit(10, "mm"))
 },
  reverse_ht = NULL,
  use_raster = NULL,
  raster_device = "png",
  raster_by_magick = TRUE,
  height = NULL,
  width = NULL,
  units = "inch",
  cores = 1,
  verbose = TRUE,
  seed = 11,
  legend.position = "right",
  ht_params = list(),
  ...
)

Arguments

srt

A Seurat object.

lineages

Lineage names for which dynamic features should be calculated.

features

Features to plot. By default, this parameter is set to NULL, and the dynamic features will be determined by the parameters min_expcells, r.sq, dev.expl, padjust and num_intersections.

use_fitted

Whether to use fitted values.

border

Draw borders. Kept for compatibility; more specific *_border arguments inherit this when NULL.

heatmap_border, cell_annotation_border, feature_annotation_border

Borders for the heatmap body and annotations. NULL inherits border.

heatmap_border_palcolor, cell_annotation_border_palcolor, feature_annotation_border_palcolor

Border colors when the matching border argument is TRUE.

heatmap_border_size, cell_annotation_border_size, feature_annotation_border_size

Border line widths when the matching border argument is TRUE.

flip

Flip rows and columns.

min_expcells

The minimum number of expected cells.

r.sq

The R-squared threshold.

dev.expl

The deviance explained threshold.

padjust

The p-value adjustment threshold.

num_intersections

This parameter is a numeric vector used to determine the number of intersections among lineages. It helps in selecting which dynamic features will be used. By default, when this parameter is set to NULL, all dynamic features that pass the specified threshold will be used for each lineage.

cell_density

The cell density within each cell bin. By default, this parameter is set to 1, which means that all cells will be included within each cell bin.

cell_bins

The number of cell bins.

order_by

The order of the heatmap.

layer

Assay layer to use.

assay

Assay to use. NULL uses the default assay.

exp_method

Expression transform: "zscore", "raw", "fc", "log2fc", or "log1p".

exp_legend_title

Legend title for expression.

limits

Color-scale limits (length 2).

lib_normalize, libsize

Library-size normalization and per-cell library sizes.

family

A character or character vector specifying the family of distributions to use for the GAM. If family is set to NULL, the appropriate family will be automatically determined based on the data. If length(family) is 1, the same family will be used for all features. Otherwise, family must have the same length as features.

suffix

Suffix to append to the output layer names for each lineage. Default is the lineage names.

n_candidates

A number of candidate features to select when features is NULL.

minfreq

Minimum frequency threshold for candidate features. Features with a frequency less than minfreq will be excluded.

fit_method

The method used for fitting features. Either "gam" (generalized additive models) or "pretsa" (Pattern recognition in Temporal and Spatial Analyses).

knot

For fit_method = "pretsa": B-spline knots. 0 or "auto".

max_knot_allowed

For fit_method = "pretsa" when knot = "auto": max knots.

padjust_method

The method used for p-value adjustment.

cluster_features_by

Which lineage to use when clustering features. By default, this parameter is set to NULL, which means that all lineages will be used.

cluster_rows, cluster_columns, cluster_row_slices, cluster_column_slices

Heatmap clustering.

show_row_names

Whether to draw row/column names for the heatmap body.

show_column_names

Whether to draw row/column names for the heatmap body.

row_names_side, column_names_side, row_names_rot, column_names_rot

Name placement.

row_title, column_title, row_title_side, column_title_side, row_title_rot, column_title_rot

Slice titles.

feature_split, feature_split_by, n_split, split_order, split_method, decreasing

Feature splitting. split_method is "kmeans", "hclust", or "mfuzz".

fuzzification

Mfuzz fuzzification coefficient.

anno_terms, anno_keys, anno_features

Enrichment annotations.

terms_width, terms_stat_width, terms_fontsize

Term annotation size.

terms_stat

Enrichment statistic for term bars: "none", "score" (-log10 of the active p-value), or an enrichment column such as "p.adjust".

terms_stat_digits, terms_stat_label, terms_stat_axis

Statistic labels ("none", "value", "significance", "both") and shared axis.

terms_stat_background_palcolor, terms_stat_border, terms_stat_border_palcolor, terms_stat_border_size, terms_stat_label_palcolor

Statistic-panel appearance. NULL inherits the matching terms_* setting.

terms_group_background, terms_background_palcolor, terms_background_alpha, terms_border, terms_border_palcolor, terms_border_size, terms_text_palcolor, terms_bar_palcolor

Term-block appearance. terms_text_palcolor = NULL maps text to enrichment significance; terms_bar_palcolor = NULL matches bar color to term text.

keys_width, keys_fontsize, features_width, features_fontsize

Key and feature annotations.

IDtype, species, db_combine, mirror, db, TERM2GENE, TERM2NAME, minGSSize, maxGSSize

Gene-set database (see PrepareDB).

db_update

Force a refresh. FALSE loads the cache when available.

db_version

Database version to retrieve.

convert_species

Use a species-converted database when the annotation is missing for species.

Ensembl_version

Ensembl version. NULL uses the latest.

GO_simplify, GO_simplify_cutoff, simplify_method, simplify_similarityCutoff

GO simplification.

pvalueCutoff, padjustCutoff, topTerm, show_termid, topWord, words_excluded

Enrichment filters.

nlabel, features_label, label_size, label_color

Feature labels.

pseudotime_label

The pseudotime label.

pseudotime_label_color

The pseudotime label color.

pseudotime_label_linetype

The pseudotime label line type.

pseudotime_label_linewidth

The pseudotime label line width.

heatmap_palette

Palette used for CNV heatmap values.

heatmap_palcolor

Palette used for CNV heatmap values.

pseudotime_palette

The color palette to use for pseudotime.

pseudotime_palcolor

The colors to use for the pseudotime in the heatmap.

feature_split_palette

Split colors.

feature_split_palcolor

Custom colors for feature-split annotation labels.

cell_annotation, cell_annotation_palette, cell_annotation_palcolor, cell_annotation_params

Cell annotations. Palette length should match cell_annotation.

feature_annotation, feature_annotation_palette, feature_annotation_palcolor, feature_annotation_params

Feature annotations. Palette length should match feature_annotation.

separate_annotation

Names of the annotations to be displayed in separate annotation blocks. Each name should match a column name in the metadata of the Seurat object.

separate_annotation_palette

The color palette to use for separate annotations.

separate_annotation_palcolor

The colors to use for each level of the separate annotations.

separate_annotation_params

Other parameters to ComplexHeatmap::HeatmapAnnotation when creating a separate annotation blocks.

reverse_ht

Whether to reverse the heatmap.

use_raster, raster_device, raster_by_magick

Raster device (NULL chooses automatically).

width, height, units

Heatmap size. NULL sizes from matrix dimensions.

cores

The number of worker processes to use for parallelization. Default is 1.

verbose

Whether to print the message. Default is TRUE.

seed

Optional integer seed. When supplied, every input receives a deterministic independent L'Ecuyer-CMRG random-number stream, making results reproducible across worker counts and scheduling order. The caller's random number state is restored when the call finishes.

legend.position

Legend side ("right", "left", "top", "bottom"). Gap to the heatmap grows automatically when long row names are on the right.

ht_params

Extra arguments passed to ComplexHeatmap::Heatmap, overriding defaults.

...

Additional arguments passed to helper functions.

See also

RunDynamicFeatures, RunDynamicEnrichment

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:22:37] Start standard processing workflow...
#> ℹ [2026-08-30 04:22:37] Checking a list of <Seurat>...
#> ! [2026-08-30 04:22:37] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:22:37] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:22:37] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:22:37] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:22:37] Number of available HVF: 2000
#> ℹ [2026-08-30 04:22:37] Finished check
#> ℹ [2026-08-30 04:22:37] Perform `ScaleData()`
#> ℹ [2026-08-30 04:22:38] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:22:38] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:22:38] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:22:38] Reorder clusters...
#> ℹ [2026-08-30 04:22:38] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:22:38] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:22:45] Standard processing workflow completed

pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).

pancreas_sub <- RunDynamicFeatures(
  pancreas_sub,
  lineages = c("Lineage1", "Lineage2"), ,
  fit_method = "pretsa",
  n_candidates = 200
)
#> ℹ [2026-08-30 04:22:47] Start find dynamic features
#> ℹ [2026-08-30 04:22:47] Data type is raw counts
#> ℹ [2026-08-30 04:22:48] Number of candidate features (union): 225
#> ℹ [2026-08-30 04:22:48] Data type is raw counts
#> ℹ [2026-08-30 04:22:48] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:22:48] Calculating dynamic features for "Lineage2"...
#> ✔ [2026-08-30 04:22:48] Find dynamic features done

ht1 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = "Lineage1",
  n_split = 5,
  split_method = "kmeans-peaktime",
  cell_annotation = "SubCellType",
  width = 2,
  height = 3
)
#> ℹ [2026-08-30 04:22:48] [1] 150 features from Lineage1 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
ht1$plot


thisplot::panel_fix(ht1$plot, raster = TRUE, dpi = 50)


ht2 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = "Lineage1",
  features = c(
    "Sox9",
    "Neurod2",
    "Isl1",
    "Rbp4",
    "Pyy",
    "S_score",
    "G2M_score"
  ),
  cell_annotation = "SubCellType"
)
#> ℹ [2026-08-30 04:22:51] Start find dynamic features
#> ℹ [2026-08-30 04:22:51] Data type is raw counts
#> ℹ [2026-08-30 04:22:51] Number of candidate features (union): 2
#> ℹ [2026-08-30 04:22:52] Data type is raw counts
#> ! [2026-08-30 04:22:52] Negative values detected
#> Warning: Negative values detected
#> ! [2026-08-30 04:22:52] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:22:52] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:22:52] Using 1 core
#> ⠙ [2026-08-30 04:22:52] Running for S_score [1/2] ■■■■■       50% | ETA:  0s
#> ✔ [2026-08-30 04:22:52] Completed 2 tasks in 237ms
#> 
#> ℹ [2026-08-30 04:22:52] Building results
#> ✔ [2026-08-30 04:22:52] Find dynamic features done
#> ℹ [2026-08-30 04:22:52] Some features were missing in at least one lineage: 
#> ℹ                       Isl1,Neurod2,Pyy,Rbp4,Sox9...
ht2$plot


ht3 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = c("Lineage1", "Lineage2"),
  n_split = 5,
  nlabel = 10,
  split_method = "kmeans",
  cluster_rows = TRUE,
  cell_annotation = "SubCellType",
  width = 1,
  height = 2
)
#> ℹ [2026-08-30 04:22:53] [1] 164 features from Lineage1,Lineage2 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
ht3$plot


ht4 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = c("Lineage1", "Lineage2"),
  reverse_ht = "Lineage1",
  cell_annotation = "SubCellType",
  n_split = 3,
  nlabel = 10,
  split_method = "mfuzz",
  species = "Mus_musculus",
  db = "GO_BP",
  anno_terms = TRUE,
  width = 1,
  height = 2
)
#> ℹ [2026-08-30 04:22:56] [1] 164 features from Lineage1,Lineage2 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
#> ℹ [2026-08-30 04:22:57] Start Enrichment analysis
#> ℹ [2026-08-30 04:22:57] Species: "Mus_musculus"
#> ℹ [2026-08-30 04:22:57] Loading cached: GO_BP version: 3.23.0 nterm:14957 created: 2026-08-30 04:14:47
#> ℹ [2026-08-30 04:22:58] Permform enrichment...
#> ℹ [2026-08-30 04:22:59] Using 1 core
#> ⠙ [2026-08-30 04:22:59] Running for 1 [1/3] ■■■         33% | ETA:  3s
#> ⠹ [2026-08-30 04:22:59] Running for 2 [2/3] ■■■■■■      67% | ETA:  1s
#> ✔ [2026-08-30 04:22:59] Completed 3 tasks in 4.2s
#> 
#> ℹ [2026-08-30 04:22:59] Building results
#> ✔ [2026-08-30 04:23:03] Enrichment analysis done

ht5 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = "Lineage1",
  cell_annotation = "SubCellType",
  n_split = 2,
  split_method = "mfuzz",
  species = "Mus_musculus",
  db = "GO_BP",
  cores = 2,
  nlabel = 10,
  anno_terms = TRUE,
  anno_keys = TRUE,
  anno_features = TRUE,
  width = 1,
  height = 2,
  terms_width = grid::unit(1, "in"),
  terms_fontsize = 6,
  keys_width = grid::unit(0.5, "in"),
  keys_fontsize = c(3, 6),
  features_width = grid::unit(0.5, "in"),
  features_fontsize = c(3, 6)
)
#> ℹ [2026-08-30 04:23:05] [1] 150 features from Lineage1 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
#> ℹ [2026-08-30 04:23:06] Start Enrichment analysis
#> ℹ [2026-08-30 04:23:06] Species: "Mus_musculus"
#> ℹ [2026-08-30 04:23:06] Loading cached: GO_BP version: 3.23.0 nterm:14957 created: 2026-08-30 04:14:47
#> ℹ [2026-08-30 04:23:07] Permform enrichment...
#> ℹ [2026-08-30 04:23:08] Using 2 cores
#> ⠙ [2026-08-30 04:23:08] Running for 2 [1/2] ■■■■■       50% | ETA: 25s
#> ✔ [2026-08-30 04:23:08] Completed 2 tasks in 25.1s
#> 
#> ℹ [2026-08-30 04:23:08] Building results
#> ✔ [2026-08-30 04:23:33] Enrichment analysis done

pancreas_sub <- AnnotateFeatures(
  pancreas_sub,
  species = "Mus_musculus",
  db = c("CSPA", "TF")
)
#> ℹ [2026-08-30 04:23:51] Species: "Mus_musculus"
#> ℹ [2026-08-30 04:23:51] Loading cached: TF version: AnimalTFDB4 nterm:2 created: 2026-08-30 04:00:31
#> ℹ [2026-08-30 04:23:51] Preparing database: CSPA
ht6 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = c("Lineage1", "Lineage2"),
  reverse_ht = "Lineage1",
  use_fitted = TRUE,
  n_split = 3,
  nlabel = 10,
  split_method = "mfuzz",
  heatmap_palette = "viridis",
  cell_annotation = c(
    "SubCellType", "Phase", "G2M_score"
  ),
  cell_annotation_palette = c(
    "Chinese", "simspec", "Purples"
  ),
  separate_annotation = list(
    "SubCellType", c("Arxes1", "Ncoa2")
  ),
  separate_annotation_palette = c(
    "Chinese", "Set1"
  ),
  separate_annotation_params = list(
    height = grid::unit(10, "mm")
  ),
  feature_annotation = c("TF", "CSPA"),
  feature_annotation_palcolor = list(
    c("gold", "steelblue"),
    c("forestgreen")
  ),
  pseudotime_label = 25,
  pseudotime_label_color = "red",
  width = 1,
  height = 2
)
#> ℹ [2026-08-30 04:23:53] [1] 164 features from Lineage1,Lineage2 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
#> ℹ [2026-08-30 04:23:53] Start find dynamic features
#> ℹ [2026-08-30 04:23:54] Data type is raw counts
#> ℹ [2026-08-30 04:23:54] Number of candidate features (union): 2
#> ℹ [2026-08-30 04:23:54] Data type is raw counts
#> ℹ [2026-08-30 04:23:54] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:23:54] Using 1 core
#> ⠙ [2026-08-30 04:23:54] Running for Arxes1 [1/2] ■■■■■       50% | ETA:  0s
#> ✔ [2026-08-30 04:23:54] Completed 2 tasks in 131ms
#> 
#> ℹ [2026-08-30 04:23:54] Building results
#> ✔ [2026-08-30 04:23:55] Find dynamic features done
#> ℹ [2026-08-30 04:23:55] Start find dynamic features
#> ℹ [2026-08-30 04:23:56] Data type is raw counts
#> ℹ [2026-08-30 04:23:56] Number of candidate features (union): 2
#> ℹ [2026-08-30 04:23:56] Data type is raw counts
#> ℹ [2026-08-30 04:23:56] Calculating dynamic features for "Lineage2"...
#> ℹ [2026-08-30 04:23:56] Using 1 core
#> ⠙ [2026-08-30 04:23:56] Running for Arxes1 [1/2] ■■■■■       50% | ETA:  0s
#> ✔ [2026-08-30 04:23:56] Completed 2 tasks in 122ms
#> 
#> ℹ [2026-08-30 04:23:56] Building results
#> ✔ [2026-08-30 04:23:56] Find dynamic features done
#> Picking joint bandwidth of 15.8
#> Picking joint bandwidth of 21.8
#> Picking joint bandwidth of 15.8
#> Picking joint bandwidth of 21.8

ht7 <- DynamicHeatmap(
  pancreas_sub,
  exp_legend_title = "Z-score",
  lineages = c("Lineage1", "Lineage2"),
  reverse_ht = "Lineage1",
  use_fitted = TRUE,
  n_split = 3,
  nlabel = 10,
  split_method = "mfuzz",
  heatmap_palette = "viridis",
  cell_annotation = c(
    "SubCellType", "Phase", "G2M_score"
  ),
  cell_annotation_palette = c(
    "Chinese", "simspec", "Purples"
  ),
  separate_annotation = list(
    "SubCellType", c("Arxes1", "Ncoa2")
  ),
  separate_annotation_palette = c("Chinese", "Set1"),
  separate_annotation_params = list(width = grid::unit(10, "mm")),
  feature_annotation = c("TF", "CSPA"),
  feature_annotation_palcolor = list(
    c("gold", "steelblue"),
    c("forestgreen")
  ),
  pseudotime_label = 25,
  pseudotime_label_color = "red",
  flip = TRUE,
  column_title_rot = 90,
  width = 2,
  height = 1
)
#> ℹ [2026-08-30 04:24:00] [1] 164 features from Lineage1,Lineage2 passed the threshold (exp_ncells>[1] 20 & r.sq>[1] 0.2 & dev.expl>[1] 0.2 & padjust<[1] 0.05): 
#> ℹ                       Ins1,Ins2,Nnat,Iapp,Pyy,Lrpprc,Chgb,Cck,Slc38a5,Npy...
#> ℹ [2026-08-30 04:24:00] Start find dynamic features
#> ℹ [2026-08-30 04:24:01] Data type is raw counts
#> ℹ [2026-08-30 04:24:01] Number of candidate features (union): 2
#> ℹ [2026-08-30 04:24:01] Data type is raw counts
#> ℹ [2026-08-30 04:24:01] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:01] Using 1 core
#> ⠙ [2026-08-30 04:24:01] Running for Arxes1 [1/2] ■■■■■       50% | ETA:  0s
#> ✔ [2026-08-30 04:24:01] Completed 2 tasks in 121ms
#> 
#> ℹ [2026-08-30 04:24:01] Building results
#> ✔ [2026-08-30 04:24:01] Find dynamic features done
#> ℹ [2026-08-30 04:24:02] Start find dynamic features
#> ℹ [2026-08-30 04:24:02] Data type is raw counts
#> ℹ [2026-08-30 04:24:02] Number of candidate features (union): 2
#> ℹ [2026-08-30 04:24:03] Data type is raw counts
#> ℹ [2026-08-30 04:24:03] Calculating dynamic features for "Lineage2"...
#> ℹ [2026-08-30 04:24:03] Using 1 core
#> ℹ [2026-08-30 04:24:03] Building results
#> ✔ [2026-08-30 04:24:03] Find dynamic features done
#> Picking joint bandwidth of 15.8
#> Picking joint bandwidth of 21.8
#> Picking joint bandwidth of 15.8
#> Picking joint bandwidth of 21.8