单细胞空间转录组RCTD去卷积分析学习和整理

分类: be365经常登不上 时间: 2025-07-03 15:03:37 作者: admin 阅读: 7984

RCTD(Robust Cell Type Decomposition)，是一种用于将单细胞RNA测序数据中的细胞类型注释转移到空间转录组学数据上的方法。RCTD 通过整合单细胞和空间转录组学数据，能够较为精确地为空间点（spots）分配细胞类型或细胞类型的混合，以便更好地理解空间组织结构中的基因表达情况。

RCTD提供三种模式：

Doublet mode: 该模式为每个测序点分配1-2种细胞类型，推荐用于具有高空间分辨率的技术，如Slide-seq和MERFISH；

Full mode: 该模式为每个测序点分配任意数量的细胞类型，推荐用于空间分辨率较低的技术，如100微米分辨率的Visium；

Multi mode: 这是doublet mode的扩展版本，能够在每个测序点发现超过两种细胞类型，是全模式的替代选项。

每一种都有不同的应用场景，官网提示大多数都可以用Doublet mode

分析步骤

1.导入

rm(list = ls())

library(spacexr)

library(Matrix)

library(Seurat)

library(qs)

library(BiocParallel)

register(MulticoreParam(workers = 8, progressbar = TRUE))

# 10x Visium

sce_s <- Load10X_Spatial(data.dir = "./RawData/GSE6716963/",

filename = "GSM6716963_19G081_filtered_feature_bc_matrix.h5")

# reference

sc_dataset <- qread("sc_dataset.qs")

2.数据预处理

# 过滤

sce_s <- subset(sce_s, nCount_Spatial > 5000)

sce_s <- subset(sce_s, nCount_Spatial < 35000)

# 归一化/找高变/ScaleData

sce_s <- NormalizeData(sce_s)

sce_s <- FindVariableFeatures(sce_s)

sce_s <- ScaleData(sce_s)

# PCA/UMAP

sce_s <- RunPCA(sce_s)

sce_s <- RunUMAP(sce_s, reduction = "pca", dims = 1:50)

# FindNeihbors/Cluster

sce_s <- FindNeighbors(sce_s, reduction = "pca", dims=1:50)

sce_s <- FindClusters(sce_s, resolution = 0.5, verbose = FALSE)

Idents(sce_s) <- sce_s$Spatial_snn_res.0.5

all.markers <- FindAllMarkers(sce_s, only.pos = T)

head(all.markers)

# reference处理##################

# reference细胞数量少一点，多了就好慢

ref <- subset(sc_dataset,downsample = 2000)

ref <- SCTransform(sc_dataset,ncells = 3000,

verbose = FALSE) %>%

RunPCA(verbose = FALSE) %>%

RunUMAP(dims = 1:30)

# 建议大家都不要用特殊符号

# R语言不喜欢这些特殊符号

a <- ref@meta.data

a$celltype <- sub("/","_",a$celltype)

ref@meta.data <- a

DimPlot(ref, group.by = "celltype", label = TRUE)

3.RCTD分析

input数据准备

# 如果抽样之后的细胞数太少(25个？)需要去除

ref <- subset(ref,celltype !="C3"&celltype !="C5")

# extract information to pass to the RCTD Reference function

counts <- ref[["RNA"]]$counts # 官网使用counts

cluster <- as.factor(ref$celltype)

cluster <- droplevels(cluster) # 如果去除了某些celltype

names(cluster) <- colnames(ref)

nUMI <- ref$nCount_RNA

names(nUMI) <- colnames(ref)

reference <- Reference(counts, cluster, nUMI)

# set up query with the RCTD function SpatialRNA

counts <- sce_s[["Spatial"]]@counts

coords <- GetTissueCoordinates(sce_s)

colnames(coords) <- c("x", "y")

coords[is.na(colnames(coords))] <- NULL

query <- SpatialRNA(coords, counts, colSums(counts))

RCTD-deconvolution

RCTD <- create.RCTD(query, reference, max_cores = 8)

RCTD <- run.RCTD(RCTD, doublet_mode = "doublet")

qsave(RCTD,"RCTD.qs")

sce_s <- AddMetaData(sce_s, metadata = RCTD@results$results_df)

# 通常关注这个SPOT丰度最高

p1 <- SpatialDimPlot(sce_s, group.by = "first_type")

# SPOT丰度第二高

p2 <- SpatialDimPlot(sce_s, group.by = "second_type")

p1+p2

4.RCTD结果可视化

barcodes <- colnames(RCTD@spatialRNA@counts)

weights <- RCTD@results$weights

norm_weights <- normalize_weights(weights)

# observe weight values

celltypes <- c('pericyte_SMC', 'endothelial cells','mast cells')

print(head(norm_weights[,celltypes]))

head(norm_weights)

# plot

p <- plot_puck_continuous(RCTD@spatialRNA,

barcodes,

norm_weights[,'pericyte_SMC'],

ylimit=c(0,0.5),

title='plotofDentateweights',

size=4.5,alpha=0.8);p

ggsave("Spaital_weights.pdf",width=8,height=6,plot=p,bg="white")

STdeconvolve绘制

library(STdeconvolve)

library(ggplot2)

library(ggsci)

packageVersion("STdeconvolve")

m <- as.matrix(norm_weights)

p <- coords

plt <- vizAllTopics(theta = m,

pos = p,

topicOrder=seq(ncol(m)),

topicCols=rainbow(ncol(m)),

groups = NA,

group_cols = NA,

r = 3, # size of scatterpies; adjust depending on the coordinates of the pixels

lwd = 0.3,

showLegend = TRUE,

plotTitle = "scatterpies")

## function returns a `ggplot2` object, so other aesthetics can be added on:

plt <- plt + ggplot2::guides(fill=ggplot2::guide_legend(ncol=2));plt

ggsave("Spaital_scatterpies.pdf", width=9, height=6, plot=plt, bg="white")

参考资料：

Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022 Apr;40(4):517-526

RCTD-Seirat：https://satijalab.org/seurat/articles/spatial_vignette.html#working-with-multiple-slices-in-seurat

github：https://github.com/dmcable/spacexr/tree/master/vignettes

单细胞天地：https://mp.weixin.qq.com/s/r78K4tm44VqV2FsIoS71Cg

朴素的科研打工仔：https://mp.weixin.qq.com/s/jmNw8yxqV3IsMeLAIJpubg

单细胞空间交响乐：https://mp.weixin.qq.com/s/y8gfRA91IaHt-svFKdhuHA

单细胞空转分析流程

既往推文：

单细胞空间转录组分析流程学习(一)：https://mp.weixin.qq.com/s/E4WuPokBOxKRbBF6CEB5aA

单细胞空间转录组分析流程学习(二)： https://mp.weixin.qq.com/s/8AFeq50Dc91cI_6jdQZfFg

单细胞空间转录组分析流程学习python版(三)： https://mp.weixin.qq.com/s/wt4nVi8pTFwQMNN8Ipj_mA

注：若对内容有疑惑或者有发现明确错误的朋友，请联系后台(欢迎交流)。更多内容可关注公众号：生信方舟

- END -

友情链接