Convert Seurat to an internal Giotto workflow object

Create a `giotto2` object from a Seurat object. The converter is SCT-aware: raw counts remain the default Giotto input, while SCT normalized values are optionally added as an extra Giotto expression layer. The input Seurat object is not modified.

Usage

SeuratToScopGiotto(
  srt,
  assay = NULL,
  layer = "counts",
  sct.assay = "SCT",
  use_sct = c("auto", "none", "normalized"),
  image = NULL,
  coord.cols = c("x", "y"),
  features = NULL,
  conversion_params = list(),
  use_official = TRUE,
  verbose = TRUE,
  seed = 11
)

Arguments

srt: A Seurat object.
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.
layer: Assay layer used as the expression matrix.
sct.assay: Name of the SCT assay.
use_sct: How to handle SCT data. `"auto"` keeps counts as the main Giotto expression and records SCT availability. `"none"` ignores SCT. `"normalized"` adds SCT normalized values as an additional expression layer.
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.
features: Features used for PCA and clustering. If NULL, current variable features are used, falling back to all assay features.
conversion_params: Additional parameters passed to Giotto::createGiottoObject().
use_official: Whether to try `Giotto::seuratToGiottoV5()` before falling back to the scop-controlled converter.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed for reproducibility. Default is 11.

Value

A `giotto2` object.

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
g <- structure(
  list(
    giotto = list(
      umap = cbind(
        UMAP_1 = as.numeric(scale(spatial$x)),
        UMAP_2 = as.numeric(scale(spatial$y))
      )
    ),
    source = list(
      cells = colnames(spatial),
      features = rownames(spatial),
      coordinates = data.frame(
        cell_ID = colnames(spatial),
        sdimx = spatial$x,
        sdimy = spatial$y
      )
    ),
    results = list(
      cluster = list(
        table = data.frame(
          cluster = paste0("cluster_", (seq_len(ncol(spatial)) - 1) %% 3 + 1),
          row.names = colnames(spatial)
        )
      ),
      spatial_network = list(
        table = data.frame(
          from = colnames(spatial)[1:8],
          to = colnames(spatial)[2:9]
        )
      )
    ),
    active = "cluster"
  ),
  class = c("giotto2", "list")
)

GiottoPlot(g, plot_type = "cluster")

GiottoPlot(g, plot_type = "network")


if (
  isTRUE(check_r("giotto-suite/Giotto", verbose = FALSE))
) {
g <- SeuratToScopGiotto(
  spatial,
  assay = "Spatial",
  layer = "counts",
  coord.cols = c("x", "y"),
  verbose = FALSE
)
}
#> Error in check_r("giotto-suite/Giotto", verbose = FALSE): could not find function "check_r"