加载数据
此教程演示了如何存储和与 交互Seurat 中的降维信息。为了演示目的，我们将使用通过SeuratData包提供的 2，700 个 PBMC 对象。
library(Seurat)
library(SeuratData)
pbmc <- LoadData("pbmc3k", type = "pbmc3k.final")
探索新的降维结构
在 Seurat v4.0 中，存储和与降维信息的交互已生成并信息化到对象中。每个降维过程都作为对象存储在插槽中，作为指定列表的元素。可以使用[[运算符，访问所需降维信息。 例如，使用RunPCA（）运行主成分分析后，对象[['pca']]将包含PCA的结果。 通过将新元素添加到列表中，用户可以添加附加的和自定义的降维信息。 每个存储的降维信息包含特定slot中。如
- cell.embeddings：将每个细胞的坐标存储在低维空间中。
- feature.loadings：存储每个基因在降维中的权重
- feature.loadings.projected：Seurat 通常计算基因子集（例如高变异基因）的降维信息，然后将该结构投影到整个数据集（所有基因）上。该投影的结果存储在此插槽中。
- stdev：每个维度的标准偏差。
- key：为基因坐标和表达矩阵设置的列名。
- jackstraw：存储降维过程的结果。目前仅支持PCA。
- misc：存储您可能想要的任何其他信息。
要访问这些插槽，可以使用Embeddings()Loadings()Stdev()
pbmc[["pca"]]
## A dimensional reduction object with key PC_
## Number of dimensions: 50
## Projected dimensional reduction calculated: FALSE
## Jackstraw run: TRUE
## Computed using assay: RNA
head(Embeddings(pbmc, reduction = "pca")[, 1:5])
## PC_1 PC_2 PC_3 PC_4 PC_5
## AAACATACAACCAC -4.7296855 -0.5184265 -0.7623220 -2.3156790 -0.07160006
## AAACATTGAGCTAC -0.5174029 4.5918957 5.9091921 6.9118856 -1.96243034
## AAACATTGATCAGC -3.1891063 -3.4695154 -0.8313710 -2.0019985 -5.10442765
## AAACCGTGCTTCCG 12.7933021 0.1007166 0.6310221 -0.3687338 0.21838204
## AAACCGTGTATGCG -3.1288078 -6.3481412 1.2507776 3.0191026 7.84739502
## AAACGCACTGGTAC -3.1088963 0.9262125 -0.6482331 -2.3244378 -2.00526763
head(Loadings(pbmc, reduction = "pca")[, 1:5])
## PC_1 PC_2 PC_3 PC_4 PC_5
## PPBP 0.010990202 0.01148426 -0.15176092 0.10403737 0.003299077
## LYZ 0.116231706 0.01472515 -0.01280613 -0.04414540 0.049906881
## S100A9 0.115414362 0.01895146 -0.02368853 -0.05787777 0.085382309
## IGLL5 -0.007987473 0.05454239 0.04901533 0.06694722 0.004603231
## GNLY -0.015238762 -0.13375626 0.04101340 0.06912322 0.104558611
## FTL 0.118292572 0.01871142 -0.00984755 -0.01555269 0.038743505
head(Stdev(pbmc, reduction = "pca"))
## [1] 7.098420 4.495493 3.872592 3.748859 3.171755 2.545292
seurat提供 了常用的单细胞数据降维方法[RunPCA()]和 [RunTSNE()]， 使用这些功能时，所有插槽都会自动填充。
自定义降维计算
虽然没有纳入seurat，但很容易在 R 中运行多重降维（MDS）。如果您有兴趣运行 MDS， 输出将存储在 Seurat 对象中：
# Before running MDS, we first calculate a distance matrix between all pairs of cells. Here we
# use a simple euclidean distance metric on all genes, using scale.data as input
d <- dist(t(GetAssayData(pbmc, slot = "scale.data")))
# Run the MDS procedure, k determines the number of dimensions
mds <- cmdscale(d = d, k = 2)
# cmdscale returns the cell embeddings, we first label the columns to ensure downstream
# consistency
colnames(mds) <- paste0("MDS_", 1:2)
# We will now store this as a custom dimensional reduction called 'mds'
pbmc[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(pbmc))
# We can now use this as you would any other dimensional reduction in all downstream functions
DimPlot(pbmc, reduction = "mds", pt.size = 0.5)
image.png
# If you wold like to observe genes that are strongly correlated with the first MDS coordinate
pbmc <- ProjectDim(pbmc, reduction = "mds")
## MDS_ 1
## Positive: CST3, TYROBP, FCER1G, LST1, FTL, AIF1, FTH1, TYMP, FCN1, LYZ
## LGALS1, S100A9, CFD, CD68, SERPINA1, CTSS, IFITM3, SPI1, S100A8, LGALS2
## Negative: MALAT1, RPS27A, RPS27, RPL3, RPL23A, RPL21, RPL13A, RPS6, RPS3A, RPS3
## RPL9, LTB, RPSA, CD3D, RPS25, RPS18, PTPRCAP, RPS12, RPL30, RPL31
## MDS_ 2
## Positive: NKG7, PRF1, CST7, GZMA, GZMB, B2M, FGFBP2, CTSW, GNLY, HLA-C
## GZMH, SPON2, CD247, FCGR3A, CCL5, HLA-A, CCL4, GZMM, KLRD1, CLIC3
## Negative: RPL32, RPL18A, HLA-DRA, CD79A, RPL13, MS4A1, RPL11, TCL1A, RPS9, RPL12
## LINC00926, HLA-DQB1, HLA-DQA1, HLA-DRB1, RPL28, RPS2, S100A8, HLA-DMA, RPL8, RPLP1
# Display the results as a heatmap
DimHeatmap(pbmc, reduction = "mds", dims = 1, cells = 500, projected = TRUE, balanced = TRUE)
image.png
# Explore how the first MDS dimension is distributed across clusters
VlnPlot(pbmc, features = "MDS_1")
image.png
# See how the first MDS dimension is correlated with the first PC dimension
FeatureScatter(pbmc, feature1 = "MDS_1", feature2 = "PC_1")
image.png