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Run MERINGUE spatial autocorrelation analysis

Source: R/RunMERINGUE.R
RunMERINGUE.Rd

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(),
  coordinate_space = c("raw", "legacy_display"),
  backend = c("cpp", "r")
)

Arguments

srt

A Seurat object.

assay

Assay to use. NULL uses the default assay.

layer

Assay layer used for expression values.

image

Spatial image name. Required when multiple images are present; a single image is selected automatically when NULL.

coord.cols

Metadata coordinate columns used 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@tools[["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(), expressed in the selected coordinate units.

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.

coordinate_space

Coordinate system used for MERINGUE distances. The default is raw acquisition coordinates; "legacy_display" remains an explicit compatibility option.

backend

Implementation used for the Moran autocorrelation tests. "cpp" uses a scop-compiled kernel that is numerically equivalent to MERINGUE::moranTest() and MERINGUE::moranPermutationTest() and is substantially faster for permutation tests; "r" calls the original MERINGUE functions directly. When "cpp" is selected, moran_params are ignored with a warning because the compiled kernel has no extra arguments.

Value

A Seurat object with MERINGUE results stored in srt@tools[["MERINGUE"]]. Top autocorrelated features are available at srt@tools[["MERINGUE"]]$summary$top_features.

Examples

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


spatial <- RunMERINGUE(
  spatial,
  assay = "Spatial",
  coord.cols = c("x", "y"),
  mode = c("autocorrelation", "cross_correlation"),
  nfeatures = 50,
  verbose = FALSE
)