spatialPatterns | R Documentation |
Group spatially variable genes into spatial patterns using Automatic Expression Histology, using the SpatialDE Python package.
spatialPatterns(x, de_results, ...)
## S4 method for signature 'matrix'
spatialPatterns(
x,
de_results,
coordinates,
qval_thresh = 0.05,
n_patterns,
length,
verbose = FALSE
)
## S4 method for signature 'SpatialExperiment'
spatialPatterns(
x,
de_results,
qval_thresh = 0.05,
n_patterns,
length,
assay_type = "counts",
verbose = FALSE
)
x |
A numeric Alternatively, a SpatialExperiment object. |
de_results |
|
... |
For the generic, arguments to pass to specific methods. |
coordinates |
A For the SpatialExperiment method, coordinates are taken from
|
qval_thresh |
|
n_patterns |
|
length |
|
verbose |
A |
assay_type |
A |
A list
of two data.frame
s (pattern_results, patterns):
pattern_results
: data.frame
with pattern membership information for each
gene.
patterns
the posterior mean underlying expression from genes in given
spatial patterns.
Davide Corso, Milan Malfait, Lambda Moses
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
SpatialDE 1.1.3: the version of the Python package used under the hood.
The individual steps performed by this function: stabilize()
,
regress_out()
and spatial_patterns()
.
## Mock up a SpatialExperiment object wit 100 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 10, tot_genes = 3, de_genes = 1, return_SPE = TRUE)
## Run spatialDE
de_results <- spatialDE(spe)
spatial_patterns <- spatialPatterns(spe, de_results = de_results,
qval_thresh = NULL, n_patterns = 4L, length = 1.5,
verbose = FALSE
)
head(spatial_patterns$pattern_results)
head(spatial_patterns$patterns)
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