作者，追風(fēng)少年i
新的一周，新的開始遂蛀，接上一篇，10X空間轉(zhuǎn)錄組數(shù)據(jù)分析之細(xì)胞的空間依賴性干厚，這一篇我們來分析空間細(xì)胞類型和空間通路分布的依賴性李滴，如下圖：
圖片.png
我們的目的就是研究空間細(xì)胞類型分布是否與生物學(xué)通路的分布存在強(qiáng)相關(guān)性。
并且我們要用代碼來實(shí)現(xiàn)一下
library(tidyverse)
library(Seurat)
library(mistyR)
source("./misty_utilities.R")
source的misty_utilities.R內(nèi)容在文章10X空間轉(zhuǎn)錄組數(shù)據(jù)分析之細(xì)胞的空間依賴性最下面
我們需要準(zhǔn)備的內(nèi)容
- 單細(xì)胞空間聯(lián)合分析的矩陣（Seurat萍诱、cell2location均可）
- 空間每個(gè)spot的通路分析結(jié)果（富集分析悬嗓，或者其他方式的打分矩陣）
定義共定位函數(shù)
# 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裕坊，這里就不做了包竹，直接拿著分析結(jié)果往下
progeny_scores <- scale(t(progeny_scores)) ####分?jǐn)?shù)的標(biāo)準(zhǔn)化操作
slide[['progeny']] <- CreateAssayObject(counts = t(progeny_scores))
其二是單細(xì)胞空間聯(lián)合分析的矩陣
anno = read.csv(sp_sc.csv,header = T,row.names = 1)
slide[['c2l']] <- CreateAssayObject(counts = t(anno))
數(shù)據(jù)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(xì)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(shù)據(jù)分析結(jié)束后進(jìn)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(xì)胞類型分布與空間分布的依賴性熱圖
圖片.png
好了，已經(jīng)分享給大家了籍凝，生活很好周瞎，有你更好，百度文庫(kù)出現(xiàn)了大量抄襲我的文章饵蒂，對(duì)此我深表無奈声诸，我寫的文章，別人掛上去賺錢退盯，抄襲可恥彼乌，掛到百度文庫(kù)的人更可恥
