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：
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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；
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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；
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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$celltype)
ref@meta.data <- aDimPlot(ref, group.by = "celltype", label = TRUE)
3.RCTD分析
input数据准备
# 如果抽样之后的细胞数太少(25个？)需要去除
ref <- subset(ref,celltype !="C3"&celltype !="C5")# extract information to pass to the RCTD Reference function
counts <- ref[["RNA"]]$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 <- coordsplt <- 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 pixelslwd = 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：
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Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022 Apr;40(4):517-526
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RCTD-Seirat：https://satijalab.org/seurat/articles/spatial_vignette.html#working-with-multiple-slices-in-seurat
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github：https://github.com/dmcable/spacexr/tree/master/vignettes
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单细胞天地：https://mp.weixin.qq.com/s/r78K4tm44VqV2FsIoS71Cg
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朴素的科研打工仔：https://mp.weixin.qq.com/s/jmNw8yxqV3IsMeLAIJpubg
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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
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