跟着Nature学习空转细胞生态位分析（一）

生信技能树

发布于 2025-07-29 10:34:49

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2022年8月10号发表在Nature杂志上的一篇文献，标题为《Spatial multi-omic map of human myocardial infarction》，是一篇非常经典的空间转录组的细胞生态位和分子生态位学习的文章，文章不仅分享了数据和代码，还有详细的分析思路！

数据下载见：狡兔三窟：一个数据放三个地方，只有一个地方的数据是对的！

数据分析思路：

先绘制一下文章的fig1

fig1给我们展示了相关的单细胞结果以及几个具有代表性的空转的cell2location的反卷积结果：

空转：

读取单细胞数据：

使用了我前面刚刚获得的新技能：

rm(list=ls())
library(schard)
library(Seurat)

## 1.读取单细胞数据
# load h5ad as Seurat
file <- "data/All-snRNA-Spatial_multi-omic/179b3914-7e79-49f2-9f4b-31a536757ee5.h5ad"
scRNA = schard::h5ad2seurat(file)
scRNA

# An object of class Seurat 
# 28975 features across 191795 samples within 1 assay 
# Active assay: RNA (28975 features, 0 variable features)
# 2 layers present: counts, data
# 3 dimensional reductions calculated: Xharmony_, Xpca_, Xumap_

meta <- scRNA@meta.data
head(scRNA@meta.data)

DimPlot(scRNA, group.by = "cell_type_original", label = T)

总共：191795个细胞，与文献中的可以对应上

分别看看上面四个示例的空转结果

样本：Ctrl tissue (P7)：

## 2.读取空转数据
## 样本：Ctrl tissue (P7)
# Visium_control_P7.h5ad
control_P7 <- readRDS("data/ACH003/ACH003.rds")
control_P7
head(control_P7@meta.data)
colnames(control_P7@meta.data)

Assays(control_P7)
DefaultAssay(control_P7) <- "c2l"
# DefaultAssay(control_P7) <- "c2l_props"
rownames(control_P7)
# vCMs, ventricular cardiomyocytes 心室心肌细胞
SpatialFeaturePlot(control_P7, features = c("CM","Fib","Myeloid"),pt.size.factor = 800)

DefaultAssay(control_P7) <- "progeny"
rownames(control_P7)
# "TGFb"
SpatialFeaturePlot(control_P7, features = c("TGFb","NFkB"),pt.size.factor = 800)

c2l反卷积结果：

progeny分析结果：

样本：BZ tissue (P3)：

## 样本：BZ tissue (P3)
# P3,RZ/BZ_P3,group_1,BZ,10X,10X0026
BZ_P3 <- readRDS("data/10X0026/10X0026.rds")
BZ_P3
head(BZ_P3@meta.data)
colnames(BZ_P3@meta.data)

Assays(BZ_P3)
DefaultAssay(BZ_P3) <- "c2l"
#DefaultAssay(BZ_P3) <- "c2l_props"
rownames(BZ_P3)
# vCMs, ventricular cardiomyocytes 心室心肌细胞
SpatialFeaturePlot(BZ_P3, features = c("CM","Fib","Myeloid"),pt.size.factor = 500)


DefaultAssay(BZ_P3) <- "progeny"
rownames(BZ_P3)
# "TGFb"
SpatialFeaturePlot(BZ_P3, features = c("TGFb","NFkB"),pt.size.factor = 500)

c2l反卷积结果：

progeny分析结果：

样本：IZ tissue (P3)：

## 样本：IZ tissue (P3)
# P3,IZ_P3,group_2,IZ,10X,10X0017
IZ_P3 <- readRDS("data/10X0017/10X0017.rds")
IZ_P3
head(IZ_P3@meta.data)
colnames(IZ_P3@meta.data)

Assays(IZ_P3)
DefaultAssay(IZ_P3) <- "c2l"
#DefaultAssay(IZ_P3) <- "c2l_props"
rownames(IZ_P3)
# vCMs, ventricular cardiomyocytes 心室心肌细胞
SpatialFeaturePlot(IZ_P3, features = c("CM","Fib","Myeloid"),pt.size.factor = 500)

DefaultAssay(IZ_P3) <- "progeny"
rownames(IZ_P3)
# "TGFb"
SpatialFeaturePlot(IZ_P3, features = c("TGFb","NFkB"),pt.size.factor = 500)

c2l反卷积结果：

progeny分析结果：

样本：FZ tissue (P14)：

## 样本：FZ tissue (P14)
# P14,FZ_P14,group_3,FZ,ACH,ACH005
FZ_P14 <- readRDS("data/ACH005/ACH005.rds")
FZ_P14
head(FZ_P14@meta.data)
colnames(FZ_P14@meta.data)

Assays(FZ_P14)
DefaultAssay(FZ_P14) <- "c2l"
#DefaultAssay(FZ_P14) <- "c2l_props"
rownames(FZ_P14)
# vCMs, ventricular cardiomyocytes 心室心肌细胞
SpatialFeaturePlot(FZ_P14, features = c("CM","Fib","Myeloid"),pt.size.factor = 800)


DefaultAssay(FZ_P14) <- "progeny"
rownames(FZ_P14)
# "TGFb"
SpatialFeaturePlot(FZ_P14, features = c("TGFb","NFkB"),pt.size.factor = 800)

c2l反卷积结果：