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。

举例

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