monocle 2拟时序分析

library(monocle)
packageVersion("monocle")
#monocle构建CDS需要3个矩阵：expr.matrix、pd、fd
# 将Seurat中的对象转换为monocle识别的对象
#cds <- importCDS(GetAssayData(seurat.object))
#选择做拟时序的亚群
Mono_tj<-subset(seurat.object, idents = c(1,2,4,6,7))

Mono_matrix<-as(as.matrix(GetAssayData(Mono_tj,slot = "counts")), 'sparseMatrix')
#构建featuredata，一般featuredata需要两个col，一个是gene_id,一个是gene_short_name,row对应counts的rownames
feature_ann<-data.frame(gene_id=rownames(Mono_matrix),gene_short_name=rownames(Mono_matrix))
rownames(feature_ann)<-rownames(Mono_matrix)
#
Mono_fd<-new("AnnotatedDataFrame", data = feature_ann)
#
#Seurat object中的@meta.data一般会存放表型相关的信息如cluster、sample的来源、group等，所以选择将metadata转换为phenodata
sample_ann<-Mono_tj@meta.data
#rownames(sample_ann)<-colnames(Mono_matrix)

Mono_pd<-new("AnnotatedDataFrame", data =sample_ann)
#build new cell data set
Mono.cds<-newCellDataSet(Mono_matrix,phenoData =Mono_pd,featureData =Mono_fd,expressionFamily=negbinomial.size())

#查看phenodata、featuredata
head(pData(Mono.cds))
head(fData(Mono.cds))
#预处理
Mono.cds <- estimateSizeFactors(Mono.cds)
Mono.cds <- estimateDispersions(Mono.cds)
#筛选基因,这里可以根据自己的需要筛选特定的基因
disp_table <- dispersionTable(Mono.cds)
unsup_clustering_genes <- subset(disp_table, mean_expression >= 0.1)
Mono.cds <- setOrderingFilter(Mono.cds, unsup_clustering_genes$gene_id)
#用DDRtree 进行降维分析
Mono.cds <- reduceDimension(
  Mono.cds,
  max_components = 2,
  method = 'DDRTree')
#计算psudotime值
Mono.cds <- orderCells(Mono.cds)
head(pData(Mono.cds))

plot_cell_trajectory(Mono.cds,cell_size = 1)

plot_cell_trajectory(Mono.cds, color_by = "Pseudotime")

plot_cell_trajectory(Mono.cds, color_by = "seurat_clusters",cell_size = 1)