Standard single-cell and spatial workflow — RunStandardWorkflow • scop

Normalize, find variable features, scale, reduce dimensions, and cluster a Seurat object. workflow = "spatial" adds spot QC, spatial variable features, optional spatial clustering, and optional deconvolution. It is not a multi-slice integration orchestrator.

Objects that also contain a ChromatinAssay are preprocessed sequentially (default assay, then chromatin).

Usage

RunStandardWorkflow(
  object,
  prefix = "Standard",
  workflow = c("single_cell", "spatial"),
  assay = NULL,
  image = NULL,
  coord.cols = c("x", "y"),
  do_spot_qc = TRUE,
  spot_qc_params = list(),
  do_spatial_variable_features = TRUE,
  spatial_variable_features_params = list(),
  do_spatial_cluster = FALSE,
  spatial_cluster_method = "BayesSpace",
  spatial_q = NULL,
  bayesspace_params = list(),
  reference = NULL,
  reference_label = NULL,
  reference_assay = NULL,
  do_deconvolution = !is.null(reference),
  deconvolution_method = "RCTD",
  deconvolution_params = list(),
  do_normalization = NULL,
  normalization_method = "LogNormalize",
  do_HVF_finding = TRUE,
  HVF_method = "vst",
  nHVF = 2000,
  HVF = NULL,
  do_scaling = TRUE,
  vars_to_regress = NULL,
  regression_model = "linear",
  linear_reduction = "pca",
  linear_reduction_dims = 50,
  linear_reduction_dims_use = NULL,
  linear_reduction_params = list(),
  force_linear_reduction = FALSE,
  nonlinear_reduction = "umap",
  nonlinear_reduction_dims = 2,
  nonlinear_reduction_params = list(),
  force_nonlinear_reduction = TRUE,
  neighbor_metric = "euclidean",
  neighbor_k = 20L,
  cluster_algorithm = "louvain",
  cluster_resolution = 0.6,
  cores = 1L,
  verbose = TRUE,
  seed = 11,
  spatial_cluster_params = list(),
  do_spatial_qc = FALSE,
  spatial_qc_params = list(),
  spanorm_params = list(),
  spatial_data_type = "auto",
  ...,
  srt = NULL
)

Arguments

object: A Seurat object.
prefix: Prefix for intermediate object names.
workflow: "single_cell" or "spatial" (one explicitly selected spatial context).
assay: Assay to use. NULL uses the default assay.
image: Spatial image name. Required when multiple images are present; a single image is selected automatically when NULL.
coord.cols: Metadata coordinate columns used when no image is available.
do_spot_qc, spot_qc_params: Run RunSpotQC() and extra arguments.
do_spatial_variable_features, spatial_variable_features_params: Run RunSpatialVariableFeatures(). The workflow defaults set_variable_features = FALSE so expression HVFs are kept.
do_spatial_cluster, spatial_cluster_method, spatial_q, bayesspace_params: Spatial clustering with "BayesSpace", "BANKSY", or "SmoothClust". spatial_q = NULL uses ordinary clusters for BayesSpace/SmoothClust. BANKSY uses its resolution parameter and rejects a supplied spatial_q.
reference, reference_label, reference_assay: Optional single-cell reference for deconvolution.
do_deconvolution, deconvolution_method, deconvolution_params: Deconvolution ("RCTD", "SPOTlight", or "Cell2location"). NULL runs when reference or cell2location signatures are provided.
do_normalization, normalization_method: Normalize if the assay has no scaled data (NULL). One of "LogNormalize", "SCT", "TFIDF", "scran". "SCT" switches downstream steps to the "SCT" assay.
do_HVF_finding, HVF_method, nHVF, HVF: Highly variable features. HVF_method is "vst", "mvp", "disp", or "scran".
do_scaling: Force scaling via ScaleData.
vars_to_regress: Regressors used in scaling. NULL uses none.
regression_model: "linear", "poisson", or "negativebinomial".
linear_reduction, linear_reduction_dims, linear_reduction_dims_use, linear_reduction_params, force_linear_reduction: Linear reduction ("pca", "svd", "ica", "nmf", "mds", "glmpca"). linear_reduction_dims_use = NULL uses estimated dimensions, else the first 50.
nonlinear_reduction, nonlinear_reduction_dims, nonlinear_reduction_params, force_nonlinear_reduction: Nonlinear reduction ("umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", "fr").
neighbor_metric, neighbor_k: Neighbor graph ("euclidean", "cosine", "manhattan", "hamming").
cluster_algorithm, cluster_resolution: Clustering ("louvain", "slm", "leiden"). Larger cluster_resolution yields fewer clusters.
cores: Number of CPU cores. NULL lets each backend pick its own default: the process OpenMP team (OMP_NUM_THREADS) for C++ kernels and one worker for R/Python parallel backends.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed.
spatial_cluster_params: Named method-specific arguments. For BayesSpace, use either this list or the retained bayesspace_params, not both.
do_spatial_qc, spatial_qc_params: Run spatially aware RunSpotSweeper() before preprocessing. Filtering is disabled; inspect its QC labels first.
spanorm_params: Arguments for RunSpaNorm() when normalization_method = "SpaNorm". Creates a separate normalized assay; count-based backends continue to use the original assay.
spatial_data_type: Observation type: "auto", "spot", "bin", or "cell". Auto uses import provenance or image evidence, otherwise unknown. Cell observations cannot use BayesSpace or spot deconvolution in this workflow.
...: Additional arguments passed to reduction methods.
srt: Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

A Seurat object. Spatial workflows store per-stage execution and result-storage state in srt@tools[["run_standard_spatial_workflow"]], including the effective set_variable_features value, result_stored, and result_location. A failed stage signals an error with the same table in attribute standard_spatial_stages.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 23:51:23] Start standard processing workflow...
#> ℹ [2026-10-11 23:51:23] Checking a list of <Seurat>...
#> ! [2026-10-11 23:51:23] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:51:23] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:51:24] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:51:24] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:51:24] Number of available HVF: 2000
#> ℹ [2026-10-11 23:51:24] Finished check
#> ℹ [2026-10-11 23:51:24] Perform `ScaleData()`
#> ℹ [2026-10-11 23:51:24] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:51:25] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:51:25] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:51:25] Reorder clusters...
#> ℹ [2026-10-11 23:51:25] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:51:25] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:51:33] Standard processing workflow completed
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType"
)


# Use a combination of different linear
# or nonlinear dimension reduction methods
linear_reductions <- c(
  "pca", "nmf", "mds"
)
pancreas_sub <- RunStandardWorkflow(
  pancreas_sub,
  linear_reduction = linear_reductions,
  nonlinear_reduction = "umap"
)
#> ℹ [2026-10-11 23:51:34] Start standard processing workflow...
#> ℹ [2026-10-11 23:51:34] Checking a list of <Seurat>...
#> ℹ [2026-10-11 23:51:34] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-10-11 23:51:34] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:51:34] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:51:34] Number of available HVF: 2000
#> ℹ [2026-10-11 23:51:34] Finished check
#> ℹ [2026-10-11 23:51:34] Perform `ScaleData()`
#> ℹ [2026-10-11 23:51:34] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:51:35] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:51:35] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:51:35] Reorder clusters...
#> ℹ [2026-10-11 23:51:35] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:51:35] Perform umap nonlinear dimension reduction
#> ℹ [2026-10-11 23:51:43] Perform nmf linear dimension reduction
#> ℹ [2026-10-11 23:51:43] Running NMF...
#> ℹ StandardBE_ 1 
#> ℹ Positive:  Ccnd1, Spp1, Mdk, Rps2, Ldha, Pebp1, Cd24a, Dlk1, Krt8, Mgst1 
#> ℹ 	   Clu, Gapdh, Eno1, Prdx1, Cldn10, Mif, Cldn7, Npm1, Dbi, Vim 
#> ℹ 	   Sox9, Rpl12, Aldh1b1, Rplp1, Wfdc2, Krt18, Tkt, Aldoa, Hspe1, Ptma 
#> ℹ Negative:  Tmem108, Poc1a, Epn3, Wipi1, Tmcc3, Nhsl1, Fgf12, Plekho1, Tecpr2, Zbtb4 
#> ℹ 	   Gm10941, Trf, Man1c1, Hmgcs1, Nipal1, Jam3, Pgap1, Alpl, Kcnip3, Tnr 
#> ℹ 	   Gm15915, Rbp2, Cbfa2t2, Sh2d4a, Bbc3, Megf6, Naaladl2, Fam46d, Hist2h2ac, Tox2 
#> ℹ StandardBE_ 2 
#> ℹ Positive:  Spp1, Gsta3, Sparc, Vim, Atp1b1, Mt1, Dbi, Anxa2, Rps2, Id2 
#> ℹ 	   Rpl22l1, Rplp1, Mgst1, Clu, Sox9, Cldn6, Mdk, Pdzk1ip1, Bicc1, 1700011H14Rik 
#> ℹ 	   Rps12, S100a10, Cldn3, Rpl36a, Ppp1r1b, Adamts1, Serpinh1, Mt2, Ifitm2, Rpl39 
#> ℹ Negative:  Rpa3, Aacs, Tmem108, Poc1a, Epn3, Wipi1, B830012L14Rik, Tmcc3, Wsb1, Plekho1 
#> ℹ 	   Ppp2r2b, Tecpr2, Zbtb4, Haus8, Trf, Gm5420, Man1c1, Hmgcs1, Nipal1, Jam3 
#> ℹ 	   Tcerg1, Pgap1, Snrpa1, Alpl, Larp1b, Kcnip3, Tnr, Lsm12, Ptbp3, Gm15915 
#> ℹ StandardBE_ 3 
#> ℹ Positive:  Cck, Mdk, Gadd45a, Neurog3, Selm, Sox4, Btbd17, Tmsb4x, Btg2, Cldn6 
#> ℹ 	   Cotl1, Ptma, Jun, Ppp1r14a, Rps2, Ifitm2, Neurod2, Igfbpl1, Gnas, Krt7 
#> ℹ 	   Nkx6-1, Aplp1, Ppp3ca, Lrpap1, Rplp1, Hn1, Rps12, Mfng, BC023829, Smarcd2 
#> ℹ Negative:  Elovl6, Tmem108, Poc1a, Epn3, Nop56, Wipi1, B830012L14Rik, Rrp15, Rfc1, Fgf12 
#> ℹ 	   Slc20a1, Ppp2r2b, Lama1, Tecpr2, Zbtb4, Eif1ax, Fam162a, P4ha3, Gm10941, Tenm4 
#> ℹ 	   Pde4b, Gm5420, Man1c1, Hmgcs1, Pgap1, Mgst2, Larp1b, Kcnip3, Tnr, Lsm12 
#> ℹ StandardBE_ 4 
#> ℹ Positive:  Spp1, Cyr61, Krt18, Tpm1, Krt8, Myl12a, Vim, Jun, Anxa5, Tnfrsf12a 
#> ℹ 	   Csrp1, Sparc, Cldn7, Nudt19, Anxa2, Clu, Myl9, Atp1b1, Cldn3, Tagln2 
#> ℹ 	   S100a10, 1700011H14Rik, Cd24a, Rps2, Dbi, Id2, Lurap1l, Rplp1, Myl12b, Klf6 
#> ℹ Negative:  Rpa3, Elovl6, Aacs, Tmem108, Poc1a, Tmcc3, Rfc1, Plekho1, Slc20a1, Ppp2r2b 
#> ℹ 	   Lama1, Tecpr2, Gm10941, Tenm4, Pde4b, Man1c1, Nipal1, Jam3, Pgap1, Alpl 
#> ℹ 	   Mgst2, Kcnip3, Tnr, Ptbp3, Gm15915, Cntln, Ocln, Fras1, Rbp2, Cbfa2t2 
#> ℹ StandardBE_ 5 
#> ℹ Positive:  2810417H13Rik, Rrm2, Hmgb2, Dut, Pcna, Lig1, H2afz, Tipin, Tuba1b, Tk1 
#> ℹ 	   Mcm5, Dek, Tyms, Gmnn, Ran, Tubb5, Rfc2, Srsf2, Ranbp1, Orc6 
#> ℹ 	   Mcm3, Uhrf1, Gins2, Dnajc9, Mcm6, Siva1, Rfc3, Mcm7, Rpa2, Ptma 
#> ℹ Negative:  1110002L01Rik, Aacs, Wipi1, B830012L14Rik, Tmcc3, Trib1, Fgf12, Plekho1, Ppp2r2b, Lama1 
#> ℹ 	   Tenm4, Trf, Gm5420, Man1c1, Jam3, Mgst2, Kcnip3, Tnr, Gm15915, Cbfa2t2 
#> ℹ 	   Sh2d4a, Bbc3, Fkbp9, Ano6, Prkcb, Megf6, Fam46d, Slc52a3, Ankrd2, Tox2 
#> ✔ [2026-10-11 23:51:53] NMF compute completed
#> ℹ [2026-10-11 23:51:53] Use stored estimated dimensions 1:50 for Standardnmf
#> ℹ [2026-10-11 23:51:54] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:51:54] Reorder clusters...
#> ℹ [2026-10-11 23:51:54] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:51:54] Perform umap nonlinear dimension reduction
#> ℹ [2026-10-11 23:52:02] Perform mds linear dimension reduction
#> Error in RunDimsEstimate(object = srt, reduction = paste0(prefix, linear_reduction),     reduction_method = linear_reduction, use_stored = FALSE,     verbose = FALSE): No valid dimensions can be estimated for Standardmds
plist1 <- lapply(
  linear_reductions, function(lr) {
    CellDimPlot(
      pancreas_sub,
      group.by = "SubCellType",
      reduction = paste0(
        "Standard", lr, "UMAP2D"
      ),
      xlab = "", ylab = "",
      title = paste0(lr, "_umap"),
      legend.position = "none",
      theme_use = "theme_blank"
    )
  }
)
patchwork::wrap_plots(plist1)


nonlinear_reductions <- c(
  "umap", "tsne", "fr"
)
pancreas_sub <- RunStandardWorkflow(
  pancreas_sub,
  linear_reduction = "pca",
  nonlinear_reduction = nonlinear_reductions
)
#> ℹ [2026-10-11 23:52:03] Start standard processing workflow...
#> ℹ [2026-10-11 23:52:03] Checking a list of <Seurat>...
#> ℹ [2026-10-11 23:52:03] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-10-11 23:52:03] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:52:04] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:52:04] Number of available HVF: 2000
#> ℹ [2026-10-11 23:52:04] Finished check
#> ℹ [2026-10-11 23:52:04] Perform `ScaleData()`
#> ℹ [2026-10-11 23:52:04] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:52:04] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:52:04] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:52:05] Reorder clusters...
#> ℹ [2026-10-11 23:52:05] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:52:05] Perform umap nonlinear dimension reduction
#> ℹ [2026-10-11 23:52:13] Perform tsne nonlinear dimension reduction
#> ℹ [2026-10-11 23:52:13] Perform tsne nonlinear dimension reduction using Standardpca (1:23)
#> ℹ [2026-10-11 23:52:15] Perform fr nonlinear dimension reduction
#> ℹ [2026-10-11 23:52:15] Perform fr nonlinear dimension reduction using Standardpca_SNN
#> ✔ [2026-10-11 23:52:16] Standard processing workflow completed
plist2 <- lapply(
  nonlinear_reductions, function(nr) {
    CellDimPlot(
      pancreas_sub,
      group.by = "SubCellType",
      reduction = paste0(
        "Standardpca", nr, "2D"
      ),
      xlab = "", ylab = "",
      title = paste0("pca_", nr),
      legend.position = "none",
      theme_use = "theme_blank"
    )
  }
)
patchwork::wrap_plots(plist2)


data(visium_human_pancreas_sub)
spatial <- RunStandardWorkflow(
  visium_human_pancreas_sub,
  workflow = "spatial",
  assay = "Spatial",
  do_spatial_cluster = FALSE,
  spatial_cluster_method = "BayesSpace",
  do_deconvolution = FALSE,
  deconvolution_method = "RCTD",
  linear_reduction_dims = 10,
  linear_reduction_dims_use = 1:5,
  nonlinear_reduction_dims = 2,
  spatial_variable_features_params = list(nfeatures = 50)
)
#> ℹ [2026-10-11 23:52:17] Start standard spot-level spatial workflow...
#> ◌ [2026-10-11 23:52:17] Running spot-level quality control
#> ✔ [2026-10-11 23:52:17] Spot QC completed: 1986 evaluated, 1907 Pass, 79 Fail
#> ℹ   Scope assay "Spatial", layer "counts"
#> ℹ   Saved metadata column `SpotQC`
#> ℹ   Plot returned object `SpatialSpotPlot(<returned_object>, group.by = "SpotQC")`
#> ℹ [2026-10-11 23:52:17] Start standard processing workflow...
#> ℹ [2026-10-11 23:52:17] Checking a list of <Seurat>...
#> ! [2026-10-11 23:52:17] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:52:17] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:52:17] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:52:17] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:52:18] Number of available HVF: 2000
#> ℹ [2026-10-11 23:52:18] Finished check
#> ℹ [2026-10-11 23:52:18] Perform `ScaleData()`
#> ℹ [2026-10-11 23:52:18] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:52:19] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:52:19] Reorder clusters...
#> ℹ [2026-10-11 23:52:19] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:52:19] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:52:29] Standard processing workflow completed
#> ◌ [2026-10-11 23:52:29] Running spatial variable feature detection
#> ✔ [2026-10-11 23:52:29] Spatial variable features completed: 2000 tested, 50 ranked in the top set
#> ℹ   Scope method "moran" ("cpp" backend); assay "Spatial", layer "data"; 1986 spots; coordinates "raw"
#> ℹ   Saved full result in returned object tool bundle `SpatialVariableFeatures`
#> ℹ   Plot returned object `SpatialVariableFeaturePlot(<returned_object>, plot_type = "combined", assay = "Spatial", image = "slice1", coord.cols = c("x", "y"))`
#> ✔ [2026-10-11 23:52:29] Standard spot-level spatial workflow completed
SpatialSpotPlot(spatial, group.by = "SpotQC")

SpatialSpotPlot(
  spatial,
  features = spatial@tools[["SpatialVariableFeatures"]]$summary$top_features[1:2]
)


spatial_bayes <- RunStandardWorkflow(
  visium_human_pancreas_sub,
  workflow = "spatial",
  assay = "Spatial",
  do_spatial_cluster = TRUE,
  spatial_cluster_method = "BayesSpace",
  spatial_q = 3,
  do_deconvolution = FALSE,
  deconvolution_method = "RCTD",
  bayesspace_params = list(
    n.PCs = 5,
    n.HVGs = 200,
    store_sce = FALSE,
    spatial_cluster_params = list(
      nrep = 200,
      burn.in = 50,
      thin = 10,
      save.chain = FALSE
    )
  )
)
#> ℹ [2026-10-11 23:52:30] Start standard spot-level spatial workflow...
#> ◌ [2026-10-11 23:52:30] Running spot-level quality control
#> ✔ [2026-10-11 23:52:30] Spot QC completed: 1986 evaluated, 1907 Pass, 79 Fail
#> ℹ   Scope assay "Spatial", layer "counts"
#> ℹ   Saved metadata column `SpotQC`
#> ℹ   Plot returned object `SpatialSpotPlot(<returned_object>, group.by = "SpotQC")`
#> ℹ [2026-10-11 23:52:30] Start standard processing workflow...
#> ℹ [2026-10-11 23:52:30] Checking a list of <Seurat>...
#> ! [2026-10-11 23:52:30] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:52:30] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:52:30] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:52:30] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:52:30] Number of available HVF: 2000
#> ℹ [2026-10-11 23:52:30] Finished check
#> ℹ [2026-10-11 23:52:30] Perform `ScaleData()`
#> ℹ [2026-10-11 23:52:30] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:52:31] Use stored estimated dimensions 1:30 for Standardpca
#> ℹ [2026-10-11 23:52:32] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:52:32] Reorder clusters...
#> ℹ [2026-10-11 23:52:32] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:52:32] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:52:41] Standard processing workflow completed
#> ◌ [2026-10-11 23:52:41] Running spatial variable feature detection
#> ✔ [2026-10-11 23:52:42] Spatial variable features completed: 2000 tested, 2000 ranked in the top set
#> ℹ   Scope method "moran" ("cpp" backend); assay "Spatial", layer "data"; 1986 spots; coordinates "raw"
#> ℹ   Saved full result in returned object tool bundle `SpatialVariableFeatures`
#> ℹ   Plot returned object `SpatialVariableFeaturePlot(<returned_object>, plot_type = "combined", assay = "Spatial", image = "slice1", coord.cols = c("x", "y"))`
#> ℹ [2026-10-11 23:52:42] Convert <Seurat> to <SingleCellExperiment> for BayesSpace
#> ℹ [2026-10-11 23:52:42] Run BayesSpace spatial clustering with `q = 3`
#> Neighbors were identified for 1974 out of 1986 spots.
#> Fitting model...
#> Calculating labels using iterations 51 through 200.
#> ✔ [2026-10-11 23:52:48] BayesSpace completed: 1986 spots, 3 domains, domain size 131-1308
#> ℹ   Scope assay "Spatial", image "slice1", raw coordinates
#> ℹ   Saved metadata column `BayesSpace_cluster` and returned object tool bundle `BayesSpace`
#> ℹ   Plot returned object `SpatialSpotPlot(<returned_object>, group.by = "BayesSpace_cluster", image = "slice1")`
#> ✔ [2026-10-11 23:52:48] Standard spot-level spatial workflow completed
SpatialSpotPlot(spatial_bayes, group.by = "BayesSpace_cluster")