作者，追風少年i
新的一周，新的開始，接上一篇，10X空間轉錄組數據分析之細胞的空間依賴性，這一篇我們來分析空間細胞類型和空間通路分布的依賴性，如下圖：
圖片.png
我們的目的就是研究空間細胞類型分布是否與生物學通路的分布存在強相關性。
并且我們要用代碼來實現一下
library(tidyverse)
library(Seurat)
library(mistyR)
source("./misty_utilities.R")
source的misty_utilities.R內容在文章10X空間轉錄組數據分析之細胞的空間依賴性最下面
我們需要準備的內容
- 單細胞空間聯合分析的矩陣（Seurat、cell2location均可）
- 空間每個spot的通路分析結果（富集分析，或者其他方式的打分矩陣）
定義共定位函數
# Pipeline definition:
run_colocalization <- function(slide,
assay,
useful_features,
useful_features_ct,
out_label,
misty_out_alias = "./results/tissue_structure/misty/pathway_map/pm_") {
# Define assay of each view ---------------
view_assays <- list("main" = assay,
"juxta" = assay,
"para" = assay,
"intra_ct" = "c2l",
"para_ct" = "c2l")
# Define features of each view ------------
view_features <- list("main" = useful_features,
"juxta" = useful_features,
"para" = useful_features,
"intra_ct" = useful_features_ct,
"para_ct" = useful_features_ct)
# Define spatial context of each view -----
view_types <- list("main" = "intra",
"juxta" = "juxta",
"para" = "para",
"intra_ct" = "intra",
"para_ct" = "para")
# Define additional parameters (l in case of paraview,
# n of neighbors in case of juxta) --------
view_params <- list("main" = NULL,
"juxta" = 5,
"para" = 15,
"intra_ct" = NULL,
"para_ct" = 15)
misty_out <- paste0(misty_out_alias,
out_label, "_", assay)
run_misty_seurat(visium.slide = slide,
view.assays = view_assays,
view.features = view_features,
view.types = view_types,
view.params = view_params,
spot.ids = NULL,
out.alias = misty_out)
return(misty_out)
}
讀取空間的rds文件和spot打分注釋文件
slide = readRDS(spatialrds)
en_anno = read.csv(enrichment_spatial.csv) ###這里大家需要自己分析
這里的通路富集分析推薦大家使用R包progeny，PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics.
library(progeny)
if(species == "mouse"){
model <- progeny::getModel(organism = "Mouse", top = top)
}else if(species == "human"){
model <- progeny::getModel(organism = "Human", top = top)
富集分析推薦使用AUC或者GSVA，這里就不做了，直接拿著分析結果往下
progeny_scores <- scale(t(progeny_scores)) ####分數的標準化操作
slide[['progeny']] <- CreateAssayObject(counts = t(progeny_scores))
其二是單細胞空間聯合分析的矩陣
anno = read.csv(sp_sc.csv,header = T,row.names = 1)
slide[['c2l']] <- CreateAssayObject(counts = t(anno))
數據分析
assay_label <- "progeny"
assay <- assay_label
DefaultAssay(slide) <- assay
useful_features <- rownames(slide)
useful_features <- useful_features[! useful_features %in% c("TNFa")] ###通路特征
useful_features_ct <- rownames(GetAssayData(slide, assay = "c2l")) ###細胞類型特征
useful_features_ct <- useful_features_ct[! useful_features_ct %in% "prolif"]
mout <- run_colocalization(slide = slide,
useful_features = useful_features,
useful_features_ct = useful_features_ct,
out_label = slide_id,
assay = assay,
misty_out_alias = "./results/tissue_structure/misty/pathway_map/pm_")
misty_res_slide <- collect_results(mout)
數據分析結束后進行可視化
plot_folder <- paste0(mout, "/plots")
system(paste0("mkdir ", plot_folder))
pdf(file = paste0(plot_folder, "/", slide_id, "_", "summary_plots.pdf"))
mistyR::plot_improvement_stats(misty_res_slide)
mistyR::plot_view_contributions(misty_res_slide)
mistyR::plot_interaction_heatmap(misty_res_slide, "intra", cutoff = 0)
mistyR::plot_interaction_communities(misty_res_slide, "intra", cutoff = 0.5)
mistyR::plot_interaction_heatmap(misty_res_slide, "juxta_5", cutoff = 0)
mistyR::plot_interaction_communities(misty_res_slide, "juxta_5", cutoff = 0.5)
mistyR::plot_interaction_heatmap(misty_res_slide, "para_15", cutoff = 0)
mistyR::plot_interaction_communities(misty_res_slide, "para_15", cutoff = 0.5)
mistyR::plot_interaction_heatmap(misty_res_slide, "intra_ct", cutoff = 0)
mistyR::plot_interaction_heatmap(misty_res_slide, "para_ct_15", cutoff = 0)
dev.off()
最后分析得到細胞類型分布與空間分布的依賴性熱圖
圖片.png
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