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 = c(2, 3),
  nonlinear_reduction_params = list(),
  force_nonlinear_reduction = TRUE,
  neighbor_metric = "euclidean",
  neighbor_k = 20L,
  cluster_algorithm = "louvain",
  cluster_resolution = 0.6,
  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" runs a spot-level spatial workflow that wraps the original workflow with spot QC, spatial variable features, optional spatial clustering, and optional RCTD deconvolution.
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. If NULL, the first image is used when present.
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().
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 only when reference is provided.
deconvolution_method: Deconvolution method. Only "RCTD" is supported in this workflow.
deconvolution_params: Named list of additional arguments passed to RunRCTD().
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 to scale the data using the Seurat::ScaleData function.
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 c(2, 3).
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.
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.

Examples

library(Matrix)
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
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"
)
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
)
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)

if (requireNamespace("scran", quietly = TRUE)) {
  data(pancreas_sub)
  pancreas_scran <- pancreas_sub[, 1:80]
  pancreas_scran <- standard_scop(
    pancreas_scran,
    assay = "RNA",
    do_normalization = TRUE,
    normalization_method = "scran",
    do_HVF_finding = TRUE,
    HVF_method = "scran",
    nHVF = 100,
    linear_reduction_dims = 10,
    linear_reduction_dims_use = 1:5,
    nonlinear_reduction = "umap",
    nonlinear_reduction_dims = 2
  )
  CellDimPlot(
    pancreas_scran,
    reduction = "StandardUMAP2D",
    group.by = "Standardclusters"
  )
}

if (FALSE) { # \dontrun{
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)
)
SpatialSpotPlot(spatial, group.by = "SpotQC")
SpatialSpotPlot(spatial, group.by = "Standardclusters")
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
    )
  )
)
SpatialSpotPlot(spatial_bayes, group.by = "BayesSpace_cluster")

data(panc8_sub)
spatial_rctd <- standard_scop(
  visium_human_pancreas_sub,
  workflow = "spatial",
  assay = "Spatial",
  do_spatial_cluster = FALSE,
  spatial_cluster_method = "BayesSpace",
  do_deconvolution = TRUE,
  deconvolution_method = "RCTD",
  reference = panc8_sub,
  reference_assay = "RNA",
  reference_label = "celltype",
  deconvolution_params = list(
    max_cores = 1,
    min_cells = 25
  )
)
SpatialSpotPlot(spatial_rctd, group.by = "RCTD_dominant_type")

rctd_cols <- grep(
  "^RCTD_prop_",
  colnames(spatial_rctd@meta.data),
  value = TRUE
)
SpatialSpotPlot(
  spatial_rctd,
  group.by = rctd_cols[1:min(4, length(rctd_cols))]
)
SpatialSpotPlot(
  spatial_rctd,
  group.by = "RCTD_dominant_type",
  plot_type = "pie"
)
} # }