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Run RCTD spatial deconvolution

Source: R/RunRCTD.R
RunRCTD.Rd

Estimate spot-level cell-type proportions using RCTD and an annotated single-cell reference.

Usage

RunRCTD(
  object,
  reference,
  reference_label = "celltype",
  assay = NULL,
  reference_assay = NULL,
  layer = "counts",
  reference_layer = "counts",
  features = NULL,
  image = NULL,
  coord.cols = c("x", "y"),
  rctd_mode = c("full", "multi", "doublet"),
  max_cores = 1,
  min_cells = 25,
  prefix = "RCTD",
  store_results = TRUE,
  round_counts = TRUE,
  create_rctd_params = list(),
  run_rctd_params = list(),
  verbose = TRUE,
  ...,
  coordinate_space = c("raw", "legacy_display"),
  tool_name = "RCTD",
  srt = NULL
)

Arguments

object

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 object. If NULL, the default assay is used.

reference_assay

Assay used in reference. Only cells present in this assay are used, with reference labels aligned by cell ID.

layer, reference_layer

Assay layers used for spatial and reference raw counts.

features

Features used for RCTD. If NULL, shared features are used. Total UMI depths are computed from each full selected assay/layer before feature filtering (and after optional count rounding), not from this subset.

image

Name of the Seurat spatial image used to recover coordinates when coord.cols are not available.

coord.cols

Metadata coordinate columns used when no image coordinate source is requested or available.

rctd_mode

RCTD mode passed to spacexr. "full" is the default for Visium spot deconvolution.

max_cores

Number of cores passed to spacexr.

min_cells

Minimum number of reference cells required for each cell type after selected-assay alignment and zero-count filtering. Old spacexr RCTD requires at least 25 cells per type.

prefix

Prefix for metadata columns.

store_results

Whether to store detailed RCTD results in srt@tools.

round_counts

Whether to round non-integer values before passing data to spacexr. RCTD requires integer count matrices; rounding is a compatibility option and does not recover original counts.

create_rctd_params

Additional parameters passed to spacexr::createRctd() or spacexr::create.RCTD().

run_rctd_params

Additional parameters passed to spacexr::runRctd() or spacexr::run.RCTD().

verbose

Whether to print the message. Default is TRUE.

...

Additional parameters passed to the RCTD run step.

coordinate_space

Coordinate space used for distance-sensitive input. The default is raw acquisition coordinates, so backend distances use raw coordinate units. Use "legacy_display" explicitly to reproduce the display-scaled coordinates used before scop 0.9.0.

tool_name

Name used to store the plain result bundle in srt@tools.

srt

Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

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

Examples

data(visium_human_pancreas_sub)
data(panc8_sub)
keep_spots <- unique(round(seq(
  1,
  ncol(visium_human_pancreas_sub),
  length.out = 120
)))
spatial <- visium_human_pancreas_sub[, keep_spots]
# Results are conditional on the three reference cell types used here.
reference <- panc8_sub[, panc8_sub@meta.data[["celltype"]] %in%
  c("ductal", "alpha", "beta")]
reference <- Seurat::FindVariableFeatures(reference, nfeatures = 300, verbose = FALSE)
features_use <- head(intersect(
  SeuratObject::VariableFeatures(reference),
  rownames(spatial)
), 300)
spatial <- RunRCTD(
  object = spatial,
  reference = reference,
  reference_label = "celltype",
  assay = "Spatial",
  reference_assay = "RNA",
  layer = "counts",
  reference_layer = "counts",
  features = features_use,
  coord.cols = c("x", "y"),
  rctd_mode = "full",
  max_cores = 1,
  min_cells = 25,
  prefix = "RCTD",
  verbose = FALSE
)
#> Begin: process_cell_type_info
#> process_cell_type_info: number of cells in reference: 1148
#> process_cell_type_info: number of genes in reference: 97
#> 
#> ductal   beta  alpha 
#>    237    428    483 
#> End: process_cell_type_info
#> create.RCTD: getting regression differentially expressed genes: 
#> get_de_genes: ductal found DE genes: 22
#> get_de_genes: beta found DE genes: 4
#> get_de_genes: alpha found DE genes: 1
#> get_de_genes: total DE genes: 27
#> create.RCTD: getting platform effect normalization differentially expressed genes: 
#> get_de_genes: ductal found DE genes: 26
#> get_de_genes: beta found DE genes: 7
#> get_de_genes: alpha found DE genes: 2
#> get_de_genes: total DE genes: 35
#> fitBulk: decomposing bulk
#> chooseSigma: using initial Q_mat with sigma =  1
#> Likelihood value: 4605.44465250967
#> Sigma value:  0.84
#> Likelihood value: 4509.24334104243
#> Sigma value:  0.69
#> Likelihood value: 4433.7443755185
#> Sigma value:  0.61
#> Likelihood value: 4402.86433424729
#> Sigma value:  0.53
#> Likelihood value: 4381.61430997094
#> Sigma value:  0.46
#> Likelihood value: 4373.21937275515
#> Sigma value:  0.45
#> Likelihood value: 4372.94033462053
#> Sigma value:  0.45
SpatialDeconvolutionPlot(
  spatial,
  tool_name = "RCTD",
  plot_type = "dominant",
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)