From aa4d6aff69d01675e06496a6b38c912eafd488f8 Mon Sep 17 00:00:00 2001
From: shreyarajeshrao We can use the Plotting image 24 shows that the supposed co-localisation occurs due
-to the dense cluster of cells near the top of the image.Accounting for tissue inhomogeneity
pairwiseAssoc[["Keratin_Tumour__Neutrophils"]]
#> [1] 0.9024836
+
plotImage
function to plot any pair of
+cell types for a specific image and visually inspect cellular
+relationships.
-
# obtain colData for image 24
-cData = colData(kerenSPE) |> as.data.frame() |>
- dplyr::filter(imageID == "24")
-
-# obtain cells present in image 24
-coords = spatialCoords(kerenSPE) |> as.data.frame()
-coords$cellID = rownames(coords)
-coords = coords |> dplyr::filter(cellID %in% cData$CellID)
-
-cData$X = coords$x
-cData$Y = coords$y
-
-cData = cData |>
- dplyr::mutate(cellTypeNew = ifelse(cellType %in% c("Keratin_Tumour", "Neutrophils"),
- cellType, "Other"))
-
- pal = setNames(c("#d6b11c", "#850f07"),
- c("Keratin_Tumour", "Neutrophils"))
-
-ggplot() +
- stat_density_2d(data = cData, aes(x = X, y = Y, fill = after_stat(density)),
- geom = "raster",
- contour = FALSE) +
- geom_point(data = cData |> filter(cellType != "Other"),
- aes(x = X, y = Y, colour = cellTypeNew), size = 1) +
- scale_color_manual(values = pal) +
- scale_fill_distiller(palette = "Blues", direction = 1) +
- theme_classic() +
- labs(title = "image ID: 24")
plotImage(kerenSPE, "24", from = "Keratin_Tumour", to = "Neutrophils")
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