Standard workflow for scop — standard

This function performs a standard single-cell or spot-level spatial analysis workflow.

Usage

standard_scop(
  srt,
  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,
  ...
)

Arguments

srt: A Seurat object.
prefix: A prefix to add to the names of intermediate objects created by the function. Default is "Standard".
workflow: Workflow to run. "single_cell" keeps the original standard workflow. "spatial" is a basic, single-image Visium-style spot workflow that wraps the original workflow with spot QC, spatial variable features, optional spatial clustering, and optional deconvolution. It is not a multi-slice integration orchestrator.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.
image: Name of the Seurat spatial image used by the spatial workflow. Required when multiple images are present; a single image is selected automatically when NULL.
coord.cols: Metadata coordinate columns used by the spatial workflow when no image is available.
do_spot_qc: Whether to run RunSpotQC() in the spatial workflow.
spot_qc_params: Named list of additional arguments passed to RunSpotQC().
do_spatial_variable_features: Whether to run RunSpatialVariableFeatures() in the spatial workflow.
spatial_variable_features_params: Named list of additional arguments passed to RunSpatialVariableFeatures(). The spatial workflow defaults set_variable_features to FALSE so expression-based highly variable features are preserved; pass it explicitly to override this workflow default.
do_spatial_cluster: Whether to run spatial-aware clustering in the spatial workflow.
spatial_cluster_method: Spatial clustering method. Only "BayesSpace" is supported in this workflow.
spatial_q: Number of spatial clusters for RunBayesSpace(). If NULL, the number of ordinary spot clusters is used.
bayesspace_params: Named list of additional arguments passed to RunBayesSpace().
reference: Optional single-cell reference used for spatial deconvolution.
reference_label: Metadata column in reference containing cell type labels.
reference_assay: Assay used in reference for deconvolution.
do_deconvolution: Whether to run deconvolution in the spatial workflow. If NULL, deconvolution is run when reference or cell2location reference_signatures are provided.
deconvolution_method: Deconvolution method. One of "RCTD", "SPOTlight", or "Cell2location".
deconvolution_params: Named list of additional arguments passed to RunRCTD(), RunSPOTlight(), or RunCell2location().
do_normalization: Whether to perform normalization. If NULL, normalization will be performed if the specified assay does not have scaled data.
normalization_method: The method to use for normalization. Options are "LogNormalize", "SCT", "TFIDF", or "scran". When "SCT" is used on an RNA assay, downstream reductions and clustering are run on the generated "SCT" assay. Default is "LogNormalize".
do_HVF_finding: Whether to perform high variable feature finding. If TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.
HVF_method: The method to use for finding highly variable features. Options are "vst", "mvp", "disp", or "scran". Default is "vst".
nHVF: The number of highly variable features to select. If NULL, all highly variable features will be used. Default is 2000.
HVF: A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.
do_scaling: Whether to perform scaling. If TRUE, the function will force scaling with the package ScaleData path.
vars_to_regress: A vector of feature names to use as regressors in the scaling step. If NULL, no regressors will be used.
regression_model: The regression model to use for scaling. Options are "linear", "poisson", or "negativebinomial". Default is "linear".
linear_reduction: The linear dimensionality reduction method to use. Options are "pca", "svd", "ica", "nmf", "mds", or "glmpca". Default is "pca".
linear_reduction_dims: The number of dimensions to keep after linear dimensionality reduction. Default is 50.
linear_reduction_dims_use: The dimensions to use for downstream analysis. If NULL, estimated dimensions stored in the linear reduction will be used when available; otherwise, the first up to 50 dimensions will be used as a fallback.
linear_reduction_params: A list of parameters to pass to the linear dimensionality reduction method.
force_linear_reduction: Whether to force linear dimensionality reduction even if the specified reduction is already present in the Seurat object.
nonlinear_reduction: The nonlinear dimensionality reduction method to use. Options are "umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", or "fr". Default is "umap".
nonlinear_reduction_dims: The number of dimensions to keep after nonlinear dimensionality reduction. If a vector is provided, different numbers of dimensions can be specified for each method. Default is 2.
nonlinear_reduction_params: A list of parameters to pass to the nonlinear dimensionality reduction method.
force_nonlinear_reduction: Whether to force nonlinear dimensionality reduction even if the specified reduction is already present in the Seurat object. Default is TRUE.
neighbor_metric: The distance metric to use for finding neighbors. Options are "euclidean", "cosine", "manhattan", or "hamming". Default is "euclidean".
neighbor_k: The number of nearest neighbors to use for finding neighbors. Default is 20.
cluster_algorithm: The clustering algorithm to use. Options are "louvain", "slm", or "leiden". Default is "louvain".
cluster_resolution: The resolution parameter to use for clustering. Larger values result in fewer clusters. Default is 0.6.
cores: Number of CPU cores used by supported preprocessing steps. Default is 1.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed for reproducibility. Default is 11.
...: Additional parameters to pass to the dimensionality reduction methods.

Value

A Seurat object. Completed or partial spatial workflows store the per-stage state in srt@tools[["standard_spatial_scop"]]. The spatial variable-feature stage records its effective set_variable_features value and the before/after feature counts. If a stage fails, the signaled error carries the same table in its standard_spatial_stages attribute with that stage marked "failed".

Examples

library(Matrix)
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-07-15 19:47:57] Start standard processing workflow...
#> ℹ [2026-07-15 19:47:57] Checking a list of <Seurat>...
#> ! [2026-07-15 19:47:57] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-15 19:47:57] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:47:57] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:47:58] Use the separate HVF from `srt_list`
#> ℹ [2026-07-15 19:47:58] Number of available HVF: 2000
#> ℹ [2026-07-15 19:47:58] Finished check
#> ℹ [2026-07-15 19:47:58] Perform `ScaleData()`
#> ℹ [2026-07-15 19:47:58] Perform pca linear dimension reduction
#> ℹ [2026-07-15 19:47:59] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-15 19:47:59] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:48:24] Reorder clusters...
#> ℹ [2026-07-15 19:48:25] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:48:25] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-15 19:48:30] 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 <- standard_scop(
  pancreas_sub,
  linear_reduction = linear_reductions,
  nonlinear_reduction = "umap"
)
#> ℹ [2026-07-15 19:48:31] Start standard processing workflow...
#> ℹ [2026-07-15 19:48:31] Checking a list of <Seurat>...
#> ℹ [2026-07-15 19:48:31] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-07-15 19:48:31] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:48:32] Use the separate HVF from `srt_list`
#> ℹ [2026-07-15 19:48:32] Number of available HVF: 2000
#> ℹ [2026-07-15 19:48:32] Finished check
#> ℹ [2026-07-15 19:48:32] Perform `ScaleData()`
#> ℹ [2026-07-15 19:48:32] Perform pca linear dimension reduction
#> ℹ [2026-07-15 19:48:33] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-15 19:48:34] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:49:32] Reorder clusters...
#> ℹ [2026-07-15 19:49:33] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:49:33] Perform umap nonlinear dimension reduction
#> Warning: Key ‘StandardpcaUMAP2D_’ taken, using ‘standardpcaumap2d_’ instead
#> ℹ [2026-07-15 19:49:39] Perform nmf linear dimension reduction
#> ℹ [2026-07-15 19:49:39] 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 
#> ✔ [2026-07-15 19:49:49] NMF compute completed
#> ℹ [2026-07-15 19:49:49] Use stored estimated dimensions 1:50 for Standardnmf
#> ℹ [2026-07-15 19:49:50] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:51:31] Reorder clusters...
#> ℹ [2026-07-15 19:51:31] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:51:31] Perform umap nonlinear dimension reduction
#> ℹ [2026-07-15 19:51:38] Perform mds linear dimension reduction
#> ℹ [2026-07-15 19:51:39] Use stored estimated dimensions 1:50 for Standardmds
#> ℹ [2026-07-15 19:51:40] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:54:02] Reorder clusters...
#> ℹ [2026-07-15 19:54:02] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:54:02] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-15 19:54:10] Standard processing workflow completed
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 <- standard_scop(
  pancreas_sub,
  linear_reduction = "pca",
  nonlinear_reduction = nonlinear_reductions
)
#> ℹ [2026-07-15 19:54:11] Start standard processing workflow...
#> ℹ [2026-07-15 19:54:11] Checking a list of <Seurat>...
#> ℹ [2026-07-15 19:54:12] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-07-15 19:54:12] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:54:12] Use the separate HVF from `srt_list`
#> ℹ [2026-07-15 19:54:12] Number of available HVF: 2000
#> ℹ [2026-07-15 19:54:12] Finished check
#> ℹ [2026-07-15 19:54:12] Perform `ScaleData()`
#> ℹ [2026-07-15 19:54:12] Perform pca linear dimension reduction
#> ℹ [2026-07-15 19:54:13] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-15 19:54:14] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:57:42] Reorder clusters...
#> ℹ [2026-07-15 19:57:46] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:57:46] Perform umap nonlinear dimension reduction
#> ℹ [2026-07-15 19:57:55] Perform tsne nonlinear dimension reduction
#> ℹ [2026-07-15 19:57:55] Perform tsne nonlinear dimension reduction using Standardpca (1:23)
#> ℹ [2026-07-15 19:57:58] Perform fr nonlinear dimension reduction
#> ℹ [2026-07-15 19:57:58] Perform fr nonlinear dimension reduction using Standardpca_SNN
#> ✔ [2026-07-15 19:57:59] 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 <- standard_scop(
  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 [2026-07-15 19:58:00] Start standard spot-level spatial workflow...
#> ◌ [2026-07-15 19:58:00] Running spot-level quality control
#> ✔ [2026-07-15 19:58:00] 1907 spots passed QC and 79 spots failed QC
#> ℹ [2026-07-15 19:58:00] Start standard processing workflow...
#> ℹ [2026-07-15 19:58:00] Checking a list of <Seurat>...
#> ! [2026-07-15 19:58:00] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-15 19:58:00] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:58:00] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:58:00] Use the separate HVF from `srt_list`
#> ℹ [2026-07-15 19:58:00] Number of available HVF: 2000
#> ℹ [2026-07-15 19:58:00] Finished check
#> ℹ [2026-07-15 19:58:00] Perform `ScaleData()`
#> ℹ [2026-07-15 19:58:01] Perform pca linear dimension reduction
#> ℹ [2026-07-15 19:58:03] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:58:25] Reorder clusters...
#> ℹ [2026-07-15 19:58:25] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:58:25] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-15 19:58:37] Standard processing workflow completed
#> ◌ [2026-07-15 19:58:37] Running spatial variable feature detection
#> ✔ [2026-07-15 19:58:39] Stored 50 spatial variable features
#> ✔ [2026-07-15 19:58:39] Standard spot-level spatial workflow completed
SpatialSpotPlot(spatial, group.by = "SpotQC")

SpatialSpotPlot(
  spatial,
  features = spatial@misc[["SpatialVariableFeatures"]][1:2]
)


spatial_bayes <- standard_scop(
  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 [2026-07-15 19:58:41] Start standard spot-level spatial workflow...
#> ◌ [2026-07-15 19:58:41] Running spot-level quality control
#> ✔ [2026-07-15 19:58:41] 1907 spots passed QC and 79 spots failed QC
#> ℹ [2026-07-15 19:58:41] Start standard processing workflow...
#> ℹ [2026-07-15 19:58:41] Checking a list of <Seurat>...
#> ! [2026-07-15 19:58:41] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-15 19:58:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:58:41] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-15 19:58:41] Use the separate HVF from `srt_list`
#> ℹ [2026-07-15 19:58:41] Number of available HVF: 2000
#> ℹ [2026-07-15 19:58:42] Finished check
#> ℹ [2026-07-15 19:58:42] Perform `ScaleData()`
#> ℹ [2026-07-15 19:58:42] Perform pca linear dimension reduction
#> ℹ [2026-07-15 19:58:43] Use stored estimated dimensions 1:30 for Standardpca
#> ℹ [2026-07-15 19:58:44] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-15 19:59:06] Reorder clusters...
#> ℹ [2026-07-15 19:59:06] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-15 19:59:06] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-15 19:59:18] Standard processing workflow completed
#> ◌ [2026-07-15 19:59:18] Running spatial variable feature detection
#> ✔ [2026-07-15 19:59:20] Stored 2000 spatial variable features
#> ℹ [2026-07-15 19:59:20] Convert <Seurat> to <SingleCellExperiment> for BayesSpace
#> ℹ [2026-07-15 19:59:22] 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.
#> ℹ [2026-07-15 19:59:29] BayesSpace clusters stored in metadata column "BayesSpace_cluster"
#> ✔ [2026-07-15 19:59:29] Standard spot-level spatial workflow completed
SpatialSpotPlot(spatial_bayes, group.by = "BayesSpace_cluster")