Issue 150 plotCoregulationSpatial

[vdsukhov]

Adjusted the plotCoregulationSpatial function to support multiple samples, as outlined in issue #150.

I checked the refactored function with two example (code snippet is attached in the end).

I also added ellipsis (...) to allow passing extra arguments to Seurat::SpatialFeaturePlot. This should address some display issues with spatial data that users have encountered after upgrading to Seurat 5, as noted in this issue, by enabling manual adjustment of pt.size.factor.

library(fgsea)
library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(msigdbr)

# ----------------------------------------------------------------------------------------------------------------
# Example From Seurat vignette
# ----------------------------------------------------------------------------------------------------------------


InstallData("stxBrain")

brain <- LoadData("stxBrain", type = "anterior1")



brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)

brain2 <- LoadData("stxBrain", type = "posterior1")
brain2 <- SCTransform(brain2, assay = "Spatial", verbose = FALSE)

brain.merge <- merge(brain, brain2)

DefaultAssay(brain.merge) <- "SCT"
VariableFeatures(brain.merge) <- c(VariableFeatures(brain), VariableFeatures(brain2))
brain.merge <- RunPCA(brain.merge, verbose = FALSE)
brain.merge <- FindNeighbors(brain.merge, dims = 1:30)
brain.merge <- FindClusters(brain.merge, verbose = FALSE)
brain.merge <- RunUMAP(brain.merge, dims = 1:30)


brain.merge <- RunPCA(brain.merge, assay = "SCT", verbose = FALSE,
                      rev.pca = TRUE, reduction.name = "pca.rev",
                      reduction.key="PCR_", npcs = 50)

E <- brain.merge@reductions$pca.rev@feature.loadings


pathwaysDF <- msigdbr("mouse", category="H")
pathways <- split(pathwaysDF$gene_symbol, pathwaysDF$gs_name)

set.seed(1)
gesecaRes <- geseca(pathways, E, minSize = 15, maxSize = 500, center = FALSE)


topPathways <- gesecaRes[, pathway] |> head(4)
titles <- sub("HALLMARK_", "", topPathways)



ps <- plotCoregulationProfileSpatial(pathways[topPathways], 
                                     brain.merge, 
                                     title=titles,
                                     pt.size.factor=2.5,
                                     stroke=0.1)

ps <- lapply(ps, function(p) p + theme_minimal(base_size = 6))
ps <- cowplot::plot_grid(plotlist = ps, ncol=2)
ps


# ----------------------------------------------------------------------------------------------------------------
# Example From GESECA vignette
# ----------------------------------------------------------------------------------------------------------------
obj <- readRDS(url("https://alserglab.wustl.edu/files/fgsea/275_T_seurat.rds"))
obj <- UpdateSeuratObject(obj)


obj <- SCTransform(obj, assay = "Spatial", verbose = FALSE, variable.features.n = 10000)

obj <- RunPCA(obj, assay = "SCT", verbose = FALSE,
              rev.pca = TRUE, reduction.name = "pca.rev",
              reduction.key="PCR_", npcs = 50)

E <- obj@reductions$pca.rev@feature.loadings

pathwaysDF <- msigdbr("human", category="H")
pathways <- split(pathwaysDF$gene_symbol, pathwaysDF$gs_name)

set.seed(1)
gesecaRes <- geseca(pathways, E, minSize = 15, maxSize = 500, center = FALSE)

topPathways <- gesecaRes[, pathway] |> head(4)
titles <- sub("HALLMARK_", "", topPathways)

sfactors <- ScaleFactors(obj@images$slice1)
psf <- 1.5 * sfactors$fiducial / sfactors$hires

ps <- plotCoregulationProfileSpatial(pathways[topPathways], obj,
                                     title=titles, 
                                     pt.size.factor=psf,
                                     stroke=0.2)
cowplot::plot_grid(plotlist=ps, ncol=2)