一、數(shù)據(jù)集準備

1. 參考數(shù)據(jù)集（reference ）
2. 查詢數(shù)據(jù)集（query ）
3. 參考數(shù)據(jù)集和查詢數(shù)據(jù)集整合之后的數(shù)據(jù)集（anchors）

建議數(shù)據(jù)集在使用前進行NormalizeData、FindVariableFeatures、RunUMAP等常規(guī)預處理操作。

二、尋找錨點，投影數(shù)據(jù)集

library(Seurat)
library(dplyr)
library(ggforce)
#reference <- RunUMAP(reference, dims = 1:30, reduction = "pca", return.model = TRUE)
anchors <- FindTransferAnchors(reference = reference, query = query,
             dims = 1:30,#可以自己調(diào)節(jié) 
             reference.reduction = "pca")#官方建議scRNA-seq數(shù)據(jù)集使用PCA
Query <- MapQuery(anchorset = anchors, reference = reference, query = query, 
                  refdata = list(celltype = "celltype"), 
                  reference.reduction = "pca",
                  reduction.model = "umap")

常見報錯：Error: The provided reduction.model does not have a model stored. Please try running umot-learn on the object first.
解決方法：RunUMAP的時候加上return.model = TRUE

三、可視化
可視化投影結(jié)果，和參考數(shù)據(jù)集對比：

p1 <- DimPlot(reference,reduction = "umap",label = TRUE,raster=FALSE)
p2 <- DimPlot(Query, reduction = "ref.umap",label = TRUE,raster=FALSE)
p1|p2

畫個?；鶊D看不同注釋結(jié)果的擬合程度：

head(Query@meta.data)
#                         nCount_RNA nFeature_RNA Cluster celltype
#AAACCTGAGCTGTTCA.11_4       7913         2339       6      Comp
#AAAGCAAGTGCCTGTG.11_4       1845          856       2    Lrrc15
#AACACGTCAAGAAGAG.11_4      12460         2890       6      Comp
#AACCATGAGATCACGG.11_4       3497         1577       0   Col15a1
#AACGTTGAGATCTGAA.11_4       8999         2487       5      Npnt
#AACGTTGCACGAGGTA.11_4      12018         2369       4    Cxcl12
#                           Tissue        orig.ident
#AAACCTGAGCTGTTCA.11_4 Artery HFD 16WK         PS
#AAAGCAAGTGCCTGTG.11_4 Artery HFD 16WK         PS
#AACACGTCAAGAAGAG.11_4 Artery HFD 16WK         PS
#AACCATGAGATCACGG.11_4 Artery HFD 16WK         PS
#AACGTTGAGATCTGAA.11_4 Artery HFD 16WK         PS
#AACGTTGCACGAGGTA.11_4 Artery HFD 16WK         PS
#                      predicted.celltype.score predicted.celltype
#AAACCTGAGCTGTTCA.11_4                0.7243527            Col15a1
#AAAGCAAGTGCCTGTG.11_4                0.7817366              Acta2
#AACACGTCAAGAAGAG.11_4                0.6213131               Comp
#AACCATGAGATCACGG.11_4                0.9919202            Col15a1
#AACGTTGAGATCTGAA.11_4                0.8128144              Acta2
#AACGTTGCACGAGGTA.11_4                0.6597668               Comp
Query@meta.data %>%  na.omit() %>%
  gather_set_data(c(4,3,5)) %>% #選中想查看的列
  ggplot(aes(x, id = id, split = y, value = 1))  +
  geom_parallel_sets(aes(fill = celltype ), alpha = 0.3) +
  geom_parallel_sets_axes(axis.width = 0.1, color = "black", fill = "white") +
  geom_parallel_sets_labels(angle = 0) +
  theme_no_axes()

參考：Function reference ? Seurat (satijalab.org)
Cell, 2019, 177(7): 1888–1902.e21.