Here we will be using semla (Larsson et al. 2023) R Package to analyze the composition of the neighborhood of benign and neoplastic domains on spatial transcriptomics data. We calculate the radial distance from ductal, PanIN, GLTumor and PDTumor spatial domains and subset the spots in 150 microns from these domains. We then visualize the composition of the neighborhood using pie charts and barplots.
Data can be downloaded from Zenodo here.
library(semla)
library(Seurat)
library(tidyverse)
samples_info <- readxl::read_xlsx("../data/visium_samples_manifest.xlsx")
merged <- qs::qread("outputs/Spatial_Clustering/tumor_tumoradj_GoL_samples_annotated_v2_lite.qs")
cols <- CellChat::scPalette(length(levels(merged$main_annotation)))
names(cols) <- levels(merged$main_annotation)tumor <- subset(merged, subset = tissue == 'Tumor')
tumor_samples <- SplitObject(tumor, split.by = 'sample_id')
## Calculating the radial distance
tumor_samples <- lapply(tumor_samples, function(x){
x <- UpdateSeuratForSemla(x)
x@tools$Staffli@meta_data <- tibble(x@meta.data %>%
select(barcode, array_row, array_col, pxl_row_in_fullres, pxl_col_in_fullres) %>%
dplyr::rename(x = array_row, y = array_col) %>%
mutate(sampleID = 1,
barcode = rownames(.)))
x <- LoadImages(x, image_height = as.numeric(GetImageInfo(x)$height))
x <- RadialDistance(x, column_name = "main_annotation", selected_groups = c("Ducts", "PanIN/GLTumor", "PDTumor", "Immune(TLS)"), maxDist = 200, convert_to_microns = T)
x@meta.data <- mutate(x@meta.data,
PDTumor_nbors = ifelse(r_dist_PDTumor < 0 | r_dist_PDTumor > 150, "FALSE", "TRUE"),
PanIN_nbors = ifelse(`r_dist_PanIN/GLTumor` < 0 | `r_dist_PanIN/GLTumor` > 150, "FALSE", "TRUE"),
Ducts_nbors = ifelse(r_dist_Ducts < 0 | r_dist_Ducts > 150, "FALSE", "TRUE"),
TLS_nbors = ifelse(`r_dist_Immune(TLS)` < 0 | `r_dist_Immune(TLS)` > 150, "FALSE", "TRUE"))
x <- UpdateSeuratFromSemla(x)
x@images$slice1 <- NULL
return(x)
})
## Subsetting spots in 150 microns distance
tumor_Nhood_Dist <- lapply(tumor_samples, function(x){
PDTumor_nbors <- x@meta.data %>% filter(PDTumor_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
PanIN_nbors <- x@meta.data %>% filter(PanIN_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
Ducts_nbors <- x@meta.data %>% filter(Ducts_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
ImmuneTLS_nbors <- x@meta.data %>% filter(TLS_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
res <- data.frame(PDTumor_nbors = PDTumor_nbors,
PanIN_nbors = PanIN_nbors,
Ducts_nbors = Ducts_nbors,
ImmuneTLS_nbors = ImmuneTLS_nbors) %>%
rownames_to_column(var = 'domain') %>%
reshape2::melt() %>%
`colnames<-`(c("nhood", "domain", "fraction")) %>%
select(domain, nhood, fraction)
return(res)
}) %>%
bind_rows(.id = 'sample_id')
qsave(tumor_Nhood_Dist, "outputs/Misc/Tumor_Nhood_Dist.qs")
average_tumor_nhood_dist <- tumor_Nhood_Dist %>%
filter(domain != 'ImmuneTLS_nbors') %>%
group_by(domain, nhood) %>%
summarise(mean = mean(fraction))
average_tumor_nhood_dist$domain <- factor(average_tumor_nhood_dist$domain, levels = c("Ducts_nbors", "PanIN_nbors", "PDTumor_nbors"))
qsave(average_tumor_nhood_dist, "outputs/Misc/average_tumor_nhood_dist")
ggplot(average_tumor_nhood_dist, aes(x = "", y = mean, fill = nhood)) +
geom_bar(stat = "identity", width = 1, color = 'white') +
coord_polar("y") +
theme_void() +
facet_wrap(~domain) +
scale_fill_manual(values = cols)
average_TLS_nhood_dist <- tumor_Nhood_Dist %>%
filter(domain == 'ImmuneTLS_nbors') %>%
group_by(domain, nhood) %>%
summarise(mean = mean(fraction))
qsave(average_TLS_nhood_dist, "outputs/Misc/average_TLS_nhood_dist.qs")
ggplot(average_TLS_nhood_dist, aes(x = "", y = mean, fill = nhood)) +
geom_bar(stat = "identity", width = 1, color = 'white') +
coord_polar("y") +
theme_void() +
facet_wrap(~domain) +
scale_fill_manual(values = cols)gol <- subset(merged, subset = tissue == 'GoL')
gol_samples <- SplitObject(gol, split.by = 'sample_id')
gol_samples <- lapply(gol_samples, function(x){
x <- UpdateSeuratForSemla(x)
x@tools$Staffli@meta_data <- tibble(x@meta.data %>%
select(barcode, array_row, array_col, pxl_row_in_fullres, pxl_col_in_fullres) %>%
dplyr::rename(x = array_row, y = array_col) %>%
mutate(sampleID = 1,
barcode = rownames(.)))
x <- LoadImages(x, image_height = as.numeric(GetImageInfo(x)$height))
x <- RadialDistance(x, column_name = "main_annotation", selected_groups = c("Ducts", "PanIN/GLTumor"), maxDist = 200, convert_to_microns = T)
x@meta.data <- mutate(x@meta.data,
PanIN_nbors = ifelse(`r_dist_PanIN/GLTumor` < 0 | `r_dist_PanIN/GLTumor` > 150, "FALSE", "TRUE"),
Ducts_nbors = ifelse(r_dist_Ducts < 0 | r_dist_Ducts > 150, "FALSE", "TRUE"))
x <- UpdateSeuratFromSemla(x)
x@images$slice1 <- NULL
return(x)
})
gol_Nhood_Dist <- lapply(gol_samples, function(x){
PanIN_nbors <- x@meta.data %>% filter(PanIN_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
Ducts_nbors <- x@meta.data %>% filter(Ducts_nbors == 'TRUE') %>% pull(main_annotation) %>% table() %>% prop.table() %>% as.matrix()
res <- data.frame(PanIN_nbors = PanIN_nbors,
Ducts_nbors = Ducts_nbors) %>%
rownames_to_column(var = 'domain') %>%
reshape2::melt() %>%
`colnames<-`(c("nhood", "domain", "fraction")) %>%
select(domain, nhood, fraction)
return(res)
}) %>%
bind_rows(.id = 'sample_id')
qsave(gol_Nhood_Dist, "outputs/Misc/GoL_Nhood_Dist.qs")
average_gol_nhood_dist <- gol_Nhood_Dist %>%
group_by(domain, nhood) %>%
summarise(mean = mean(fraction))
average_gol_nhood_dist$domain <- factor(average_gol_nhood_dist$domain, levels = c("Ducts_nbors", "PanIN_nbors"))
qsave(average_gol_nhood_dist, "outputs/Misc/average_gol_nhood_dist.qs")
ggplot(average_gol_nhood_dist, aes(x = "", y = mean, fill = nhood)) +
geom_bar(stat = "identity", width = 1, color = 'white') +
coord_polar("y") +
theme_void() +
facet_wrap(~domain) +
scale_fill_manual(values = cols)