multiGenePlots {spatialDE} | R Documentation |
Plot Spatial Patterns of Multiple Genes
multiGenePlots(x, ...) ## S4 method for signature 'matrix' multiGenePlots( x, coordinates, genes_plot, viridis_option = "D", ncol = 2, point_size = 1, dark_theme = TRUE ) ## S4 method for signature 'SpatialExperiment' multiGenePlots( x, assay_type = "counts", genes_plot, viridis_option = "D", ncol = 2, point_size = 1, dark_theme = TRUE )
x |
A numeric Alternatively, a SpatialExperiment object. |
... |
For the generic, arguments to pass to specific methods. |
coordinates |
A For the SpatialExperiment method, coordinates are taken from
|
genes_plot |
character vector specifying which genes are to be plotted. |
viridis_option |
This function uses the |
ncol |
Number of columns to arrange the plots. |
point_size |
Point size of each plot. |
dark_theme |
Whether dark background should be used; this is helpful to
highlight cells with high expression when using the |
assay_type |
A |
This function draws a plot for each specified genes
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()
,
spatialDE()
.
For further analysis of the DE results:
model_search()
and spatial_patterns()
.
## Mock up a SpatialExperiment object wit 100 cells, 200 genes set.seed(42) spe <- mockSVG(size = 10, tot_genes = 200, de_genes = 10, return_SPE = TRUE) ## Run spatialDE results <- spatialDE(spe) ordered_spe_results <- results[order(results$qval), ] head(ordered_spe_results) plots <- multiGenePlots(spe, assay_type = "counts", ordered_spe_results[1:4, ]$g, point_size = 4, viridis_option = "D" )