spatial_patterns {spatialDE}

R Documentation

Group spatially variable genes into spatial patterns using automatic expression histology (AEH)

Description

Group spatially variable genes into spatial patterns using automatic expression histology (AEH)

Usage

spatial_patterns(
  x,
  coordinates,
  de_results,
  qval_thresh = 0.05,
  n_patterns,
  length,
  verbose = FALSE
)

Arguments

x	matrix-like object of normalized counts. E.g. resulting from regress_out().
coordinates	data.frame with sample coordinates. Each row is a sample, the columns with coordinates must be named 'x' and 'y'.
de_results	data.frame resulting from run().
qval_thresh	numeric scalar, specifying the q-value significance threshold to filter de_results. Only rows in de_results with qval < qval_thresh will be kept. To disable, set qval_thresh = NULL.
n_patterns	integer The number of spatial patterns
length	numeric The characteristic length scale of the clusters
verbose	logical controlling the display of the progress bar.

Value

list of two dataframe (pattern_results, patterns): pattern_results dataframe with pattern membership information for each gene. patterns the posterior mean underlying expression fro genes in given spatial patterns.

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

Examples

set.seed(42)
mock <- mockSVG(size = 10, tot_genes = 500, de_genes = 10)
stabilized <- stabilize(mock$counts)
sample_info <- mock$coordinates
sample_info$total_counts <- colSums(mock$counts)
regressed <- regress_out(counts = stabilized, sample_info = sample_info)

## Run SpatialDE
de_results <- run(x = regressed, coordinates = mock$coordinates)

## Run Spatial_patterns
sp <- spatial_patterns(
    x = regressed,
    coordinates = mock$coordinates,
    de_results = de_results,
    qval_thresh = NULL,
    n_patterns = 5, length = 1.5
)

sp$pattern_results
sp$patterns

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