课前准备----细胞分子邻域矩阵的获得与运用

#! usr/R
####zhaoyunfei
####20240728

library(Seurat)
library(dplyr)
library(dbscan)
library(ggplot2)
library(dplyr)

st.object = readRDS('/home/samples/DB/Spatial/cellniche/Muscle.spatial.rds')

st.object$pixel_x = st.object@images$CTR@coordinates$imagerow

st.object$pixel_y = st.object@images$CTR@coordinates$imagecol

st.object$sample = st.object$orig.ident

####st.object = st.object[VariableFeatures(st.object),]

xys = setNames(st.object@meta.data[, c("pixel_x", "pixel_y", "sample")], c("x", "y", "sample"))

platforms = 'Visium'

spotnames <- rownames(xys)

library(dplyr)

names(spotnames) <- c(1:nrow(xys)) %>% paste0()

et spot distances
sdist = 200
###10X空间转录组临近的6个spot
nNeighbours = 6
maxdist = 200

knn_spatial <- dbscan::kNN(x = xys[, c("x", "y")] %>% as.matrix(), k = nNeighbours)
knn_spatial.norm <- data.frame(from = rep(1:nrow(knn_spatial$id), nNeighbours),
                                 to = as.vector(knn_spatial$id),
                                 weight = 1/(1 + as.vector(knn_spatial$dist)),
                                 distance = as.vector(knn_spatial$dist))


minK = 4
# create network for coordinates
spatnet <- knn_spatial.norm
spatnet$from <- spotnames[spatnet$from]
spatnet$to <- spotnames[spatnet$to]
spatnet <- spatnet %>% group_by(from) %>% mutate(rnk = rank(distance)) %>% ungroup()
spatnet =  subset(spatnet, distance <= maxdist | rnk <= minK)

    # Add coordinates
spatnet <- cbind(spatnet, setNames(xys[spatnet$from, 1:2], paste0("start_", c("x", "y"))))
spatnet <- cbind(spatnet, setNames(xys[spatnet$to, 1:2], paste0("end_", c("x", "y"))))

####空矩阵
neighbor_mol = matrix(ncol = length(unique(spatnet$from)),nrow = length(rownames(st.object)))

for (i in 1:length(colnames(st.object))){

su = spatnet[which(spatnet$from == colnames(st.object)[i]),]

neighbor_mol[,i] = rowSums(st.object@assays$Spatial@counts[,su$to])

}

colnames(neighbor_mol) = colnames(st.object)

rownames(neighbor_mol) = rownames(st.object)

write.csv(neighbor_mol,file = 'neighbor_mol.csv',quote =F)