Run MERINGUE spatial autocorrelation analysis

Run MERINGUE spatial autocorrelation, spatial cross-correlation, and spatial module analysis for a spatial Seurat object.

Usage

RunMERINGUE(
  srt,
  assay = NULL,
  layer = "data",
  image = NULL,
  coord.cols = c("col", "row"),
  features = NULL,
  mode = c("autocorrelation", "cross_correlation", "modules"),
  nfeatures = 2000,
  min_spots = 5,
  filterDist = NA_real_,
  binary = TRUE,
  alternative = "greater",
  nperm = 0,
  ncores = 1,
  pairwise_features = NULL,
  set_variable_features = FALSE,
  store_results = TRUE,
  verbose = TRUE,
  seed = 11,
  neighbor_params = list(),
  moran_params = list(),
  cross_cor_params = list(),
  module_params = list()
)

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 for expression values.
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 to score. If NULL, current variable features are used; if no variable features are present, all assay features are used.
mode: MERINGUE analysis modes to run. "autocorrelation" computes spatial autocorrelation, "cross_correlation" computes pairwise spatial cross-correlation, and "modules" detects spatial gene modules.
nfeatures: Number of top spatial features stored in srt@misc[["SpatialVariableFeatures"]].
min_spots: Minimum number of spots with non-zero expression required for a feature to be tested.
filterDist: Euclidean distance cutoff passed to MERINGUE::getSpatialNeighbors().
binary: Whether to binarize the MERINGUE spatial neighbor matrix.
alternative: Alternative hypothesis passed to MERINGUE Moran tests.
nperm: Number of label permutations used for empirical p values. The default 0 skips p-value calculation.
ncores: Number of cores passed to MERINGUE permutation tests.
pairwise_features: Features used for spatial cross-correlation. If NULL, top spatially autocorrelated features are used.
set_variable_features: Whether to set the top spatial features as variable features for assay.
store_results: Whether to store the full result in srt@tools.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed used for permutation tests.
neighbor_params, moran_params, cross_cor_params, module_params: Named lists of additional arguments passed to the corresponding MERINGUE steps.

Value

A Seurat object with MERINGUE results stored in srt@tools[["MERINGUE"]] and top autocorrelated features stored in srt@misc[["MERINGUEFeatures"]].

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
spatial <- Seurat::NormalizeData(spatial, assay = "Spatial", verbose = FALSE)
spatial@misc[["MERINGUEFeatures"]] <- rownames(spatial)[1:4]
spatial@tools[["MERINGUE"]] <- list(
  autocorrelation = data.frame(
    feature = rownames(spatial)[1:4],
    statistic = c(0.42, 0.35, 0.28, 0.22),
    p_value = c(0.001, 0.004, 0.010, 0.020),
    q_value = c(0.004, 0.008, 0.015, 0.030)
  )
)

head(spatial@tools[["MERINGUE"]]$autocorrelation)
#>   feature statistic p_value q_value
#> 1  TMSB4X      0.42   0.001   0.004
#> 2     UBC      0.35   0.004   0.008
#> 3     GCG      0.28   0.010   0.015
#> 4    ACTB      0.22   0.020   0.030
SpatialSpotPlot(
  spatial,
  features = spatial@misc[["MERINGUEFeatures"]][1:2],
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)


if (
  isTRUE(check_r("JEFworks-Lab/MERINGUE", verbose = FALSE))
) {
spatial <- RunMERINGUE(
  spatial,
  assay = "Spatial",
  coord.cols = c("x", "y"),
  mode = c("autocorrelation", "cross_correlation"),
  nfeatures = 50,
  verbose = FALSE
)
}
#> Error in check_r("JEFworks-Lab/MERINGUE", verbose = FALSE): could not find function "check_r"