Seurat入门中……如有错误，欢迎指出
1. 如何将AnnData中的obs在Seurat中展示
和sc.pl.umap(adata, color=['cellinteraction_cluster'])不同，Seurat需要定义出Seurat object的ident，DimPlot时会默认展示这个ident
Idents(seo)<-"cellinteraction_cluster"#为meta.data中的一列
table(Idents(seo))
DimPlot(seo, reduction = "pca")
可以用group.by来展示其他meta.data的信息
DimPlot(seuratObj, reduction = "umap2",group.by = "Group")
2. 如何保留AnnData的layers的信息？
之前用scDioR进行AnnData和Seurat数据结构的转换时，总是会丢失layers的信息，比如spliced和unspliced的信息。但现在来看，不同的assays其实就相当于AnnData中的不同的layers。完全可以手动把二者提取出矩阵出来再整合。
cbmc <- CreateSeuratObject(counts = cbmc.rna)
# Add ADT data
cbmc[["ADT"]] <- CreateAssayObject(counts = cbmc.adt)
# Run analyses by specifying the assay to use
NormalizeData(object = cbmc, assay = "RNA")
NormalizeData(object = cbmc, assay = "ADT", method = "CLR")
# Retrieve and set the default assay
DefaultAssay(object = cbmc)
DefaultAssay(object = cbmc) <- "ADT"
DefaultAssay(object = cbmc)
# Pull feature expression from both assays by using keys
FetchData(object = cbmc, vars = c("rna_CD3E", "adt_CD3"))
# Plot data from multiple assays using keys
FeatureScatter(object = cbmc, feature1 = "rna_CD3E", feature2 = "adt_CD3")
3.如何保留Scanpy中Umap图的位置？
从本质上说，Umap图展示就是获得每个细胞在横纵坐标的数值，即UMAP-1和UMAP-2
该信息在AnnData中保存在.obsm['X_umap']当中，可以直接提取出来，作为meta.data的信息保存
cell_meta['UMAP1']=adata.obsm['X_umap'][:,0]
cell_meta['UMAP2']=adata.obsm['X_umap'][:,1]
而在Seurat中，当我们RunUMAP后，我们会在seo@reductions中获得一层降维的数据
> seo@reductions
$pca
A dimensional reduction object with key PC_
Number of dimensions: 50
Projected dimensional reduction calculated: FALSE
Jackstraw run: TRUE
Computed using assay: RNA
$umap
A dimensional reduction object with key UMAP_
Number of dimensions: 2
Projected dimensional reduction calculated: FALSE
Jackstraw run: FALSE
Computed using assay: RNA
$tsne
A dimensional reduction object with key tSNE_
Number of dimensions: 2
Projected dimensional reduction calculated: FALSE
Jackstraw run: FALSE
Computed using assay: RNA
每个细胞的UMAP/tSNE坐标轴值保存在该处：
> seo@reductions$tsne@cell.embeddings
tSNE_1 tSNE_2
AAACCTGAGGAGCGAG 16.717869063 -25.568710284
AAAGATGAGATGTGTA 2.652637970 -30.593065136
AAAGATGTCAGGATCT 20.795690308 -24.805323167
AAAGTAGCATGAACCT 17.382986492 -25.578860343
AAAGTAGTCATTGCCC 21.395172916 -33.082192622
因此，我们可以复制一层reduction,然后把Scanpy的Umap坐标轴信息替换
seo@reductions$umap2<-seo@reductions$umap#把在Seurat中降维的数据复制为Umap2
head(seo@reductions$umap@cell.embeddings)
scanpy_umap<-seo@meta.data[,c('UMAP1','UMAP2')]
colnames(scanpy_umap)<-c('UMAP_1','UMAP_2')
seo@reductions$umap@cell.embeddings<-as.matrix(scanpy_umap)
> seo@reductions
$pca
A dimensional reduction object with key PC_
Number of dimensions: 50
Projected dimensional reduction calculated: FALSE
Jackstraw run: TRUE
Computed using assay: RNA
$umap
A dimensional reduction object with key UMAP_
Number of dimensions: 2
Projected dimensional reduction calculated: FALSE
Jackstraw run: FALSE
Computed using assay: RNA
$tsne
A dimensional reduction object with key tSNE_
Number of dimensions: 2
Projected dimensional reduction calculated: FALSE
Jackstraw run: FALSE
Computed using assay: RNA
$umap2
A dimensional reduction object with key UMAP_
Number of dimensions: 2
Projected dimensional reduction calculated: FALSE
Jackstraw run: FALSE
Computed using assay: RNA
plot1<-DimPlot(seo, reduction = "umap",label = TRUE)+scale_color_npg()
plot2<-DimPlot(seo, reduction = "umap2",label = TRUE)+scale_color_npg()
CombinePlots(plots = list(plot1, plot2),legend="bottom")
可以看到Scanpy中Umap的坐标轴值保留了下来
