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Seurat4.0系列教程10:降维

Seurat4.0系列教程10:降维

作者: Seurat_Satija | 来源:发表于2021-05-21 12:21 被阅读0次

    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

    1. cell.embeddings:将每个细胞的坐标存储在低维空间中。
    2. feature.loadings:存储每个基因在降维中的权重
    3. 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。
    4. stdev:每个维度的标准偏差。
    5. key:为基因坐标和表达矩阵设置的列名。
    6. jackstraw:存储降维过程的结果。目前仅支持PCA。
    7. 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

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