Run SPOTlight spatial deconvolution — RunSPOTlight • scop

Estimate spot-level cell type proportions from a spatial Seurat object using a single-cell Seurat reference and the optional SPOTlight package.

Usage

RunSPOTlight(
  srt,
  reference,
  reference_label = "celltype",
  assay = NULL,
  reference_assay = NULL,
  layer = "counts",
  reference_layer = "counts",
  features = NULL,
  mgs = NULL,
  marker_top_n = 100,
  marker_min_logfc = 0,
  gene_id = "gene",
  group_id = "cluster",
  weight_id = "weight",
  min_prop = 0.01,
  scale = TRUE,
  prefix = "SPOTlight",
  tool_name = "SPOTlight",
  store_results = TRUE,
  verbose = TRUE,
  ...
)

Arguments

srt: Spatial Seurat object used as the RCTD query.
reference: Reference Seurat object containing annotated single cells.
reference_label: Metadata column in reference with cell type labels.
assay: Assay used in srt. If NULL, the default assay is used.
reference_assay: Assay used in reference.
layer, reference_layer: Assay layers used for spatial and reference raw counts.
features: Features used for RCTD. If NULL, shared features are used.
mgs: Optional marker-gene table passed to SPOTlight. It must contain columns named by gene_id, group_id, and weight_id. If NULL, a simple group-vs-rest marker table is generated from the reference expression matrix.
marker_top_n: Number of automatically generated marker genes retained per reference cell type.
marker_min_logfc: Minimum group-vs-rest log2 fold-change used when generating markers automatically.
gene_id, group_id, weight_id: Column names in mgs for gene IDs, cell type labels, and marker weights.
min_prop: Minimum cell-type proportion passed to SPOTlight.
scale: Whether SPOTlight scales expression internally.
prefix: Prefix for metadata columns.
tool_name: Name used to store detailed results in srt@tools.
store_results: Whether to store detailed RCTD results in srt@tools.
verbose: Whether to print the message. Default is TRUE.
...: Additional parameters passed to SPOTlight::SPOTlight().

Value

A Seurat object with SPOTlight proportion columns in metadata and dominant cell type summaries. When store_results = TRUE, detailed results are stored in srt@tools[[tool_name]].

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
spotlight_weights <- data.frame(
  SPOTlight_prop_Ductal = seq(0.70, 0.20, length.out = ncol(spatial)),
  SPOTlight_prop_Endocrine = seq(0.20, 0.70, length.out = ncol(spatial)),
  SPOTlight_prop_Immune = 0.10,
  row.names = colnames(spatial)
)
spotlight_weights <- spotlight_weights / rowSums(spotlight_weights)
spatial <- Seurat::AddMetaData(spatial, spotlight_weights)
spatial$SPOTlight_dominant_type <- sub(
  "^SPOTlight_prop_",
  "",
  colnames(spotlight_weights)[max.col(spotlight_weights)]
)
spatial$SPOTlight_max_prop <- apply(spotlight_weights, 1, max)

SpatialSpotPlot(
  spatial,
  group.by = "SPOTlight_dominant_type",
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)

if (requireNamespace("scatterpie", quietly = TRUE)) {
  SpatialSpotPlot(
    spatial,
    group.by = "SPOTlight_dominant_type",
    plot_type = "pie",
    overlay_image = FALSE,
    coord.cols = c("x", "y")
  )
}


if (
  requireNamespace("SPOTlight", quietly = TRUE)
) {
data(pancreas_sub)
features_use <- head(intersect(rownames(spatial), rownames(pancreas_sub)), 300)
spatial <- RunSPOTlight(
  srt = spatial,
  reference = pancreas_sub,
  reference_label = "CellType",
  assay = "Spatial",
  reference_assay = "RNA",
  features = features_use,
  marker_top_n = 20,
  verbose = FALSE
)

spotlight_cols <- grep(
  "^SPOTlight_prop_",
  colnames(spatial@meta.data),
  value = TRUE
)
SpatialSpotPlot(
  spatial,
  group.by = spotlight_cols[1:min(3, length(spotlight_cols))],
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)
}
#> Error in .filter(x[mod_genes, ], y): Insufficient number of features shared between single-cell and mixture dataset.