Seurat4.0系列教程5：交互技巧

此文演示了一些与 Seurat 对象交互的功能。为了演示，我们将使用在第一个教程中创建的 2，700 个 PBMC 对象。为了模拟我们有两个复制的情景，我们将随机分配每个集群中一半的细胞自”rep1″，另一半来自”rep2″。

library(Seurat) library(SeuratData) InstallData("pbmc3k") pbmc 在cluster ID 和replicate之间切换身份类型

# Plot UMAP, coloring cells by cell type (currently stored in object@ident) DimPlot(pbmc, reduction = "umap")

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# How do I create a UMAP plot where cells are colored by replicate? First, store the current # identities in a new column of meta.data called CellType pbmc$CellType

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# alternately : DimPlot(pbmc, reduction = 'umap', group.by = 'replicate') you can pass the # shape.by to label points by both replicate and cell type # Switch back to cell type labels Idents(pbmc) 通过cluster ID, replicate或两者兼有来列表展示细胞

# How many cells are in each cluster table(Idents(pbmc)) ## ## Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono ## 697 483 480 344 271 162 ## NK DC Platelet ## 155 32 14

# How many cells are in each replicate? table(pbmc$replicate) ## ## rep1 rep2 ## 1348 1290

# What proportion of cells are in each cluster? prop.table(table(Idents(pbmc))) ## ## Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono ## 0.264215315 0.183093252 0.181956027 0.130401820 0.102729340 0.061410159 ## NK DC Platelet ## 0.058756634 0.012130402 0.005307051

# How does cluster membership vary by replicate? table(Idents(pbmc), pbmc$replicate) ## ## rep1 rep2 ## Naive CD4 T 354 343 ## Memory CD4 T 249 234 ## CD14+ Mono 232 248 ## B 173 171 ## CD8 T 154 117 ## FCGR3A+ Mono 81 81 ## NK 81 74 ## DC 18 14 ## Platelet 6 8

prop.table(table(Idents(pbmc), pbmc$replicate), margin = 2) ## ## rep1 rep2 ## Naive CD4 T 0.262611276 0.265891473 ## Memory CD4 T 0.184718101 0.181395349 ## CD14+ Mono 0.172106825 0.192248062 ## B 0.128338279 0.132558140 ## CD8 T 0.114243323 0.090697674 ## FCGR3A+ Mono 0.060089021 0.062790698 ## NK 0.060089021 0.057364341 ## DC 0.013353116 0.010852713 ## Platelet 0.004451039 0.006201550 选择特定的细胞类型取子集

# What are the cell names of all NK cells? WhichCells(pbmc, idents = "NK") ## [1] "AAACCGTGTATGCG" "AAATTCGATTCTCA" "AACCTTACGCGAGA" "AACGCCCTCGTACA" ## [5] "AACGTCGAGTATCG" "AAGATTACCTCAAG" "AAGCAAGAGCTTAG" "AAGCAAGAGGTGTT" ## [9] "AAGTAGGATACAGC" "AATACTGAATTGGC" "AATCCTTGGTGAGG" "AATCTCTGCTTTAC" ## [13] "ACAAATTGTTGCGA" "ACAACCGAGGGATG" "ACAATTGATGACTG" "ACACCCTGGTGTTG" ## [17] "ACAGGTACTGGTGT" "ACCTGGCTAAGTAG" "ACGAACACCTTGTT" "ACGATCGAGGACTT" ## [21] "ACGCAATGGTTCAG" "ACGCTGCTGTTCTT" "ACGGAACTCAGATC" "ACGTGATGTGACAC" ## [25] "ACGTTGGAGCCAAT" "ACTGCCACTCCGTC" "ACTGGCCTTCAGTG" "ACTTCAACGTAGGG" ## [29] "AGAACAGAAATGCC" "AGATATACCCGTAA" "AGATTCCTGTTCAG" "AGCCTCTGCCAATG" ## [33] "AGCGATTGAGATCC" "AGGATGCTTTAGGC" "AGGGACGAGTCAAC" "AGTAATACATCACG" ## [37] "AGTCACGATGAGCT" "AGTTTGCTACTGGT" "ATACCACTGCCAAT" "ATACTCTGGTATGC" ## [41] "ATCCCGTGCAGTCA" "ATCTTTCTTGTCCC" "ATGAAGGACTTGCC" "ATGATAACTTCACT" ## [45] "ATGATATGGTGCTA" "ATGGACACGCATCA" "ATGGGTACATCGGT" "ATTAACGATGAGAA" ## [49] "ATTCCAACTTAGGC" "CAAGGTTGTCTGGA" "CAATCTACTGACTG" "CACCACTGGCGAAG" ## [53] "CACGGGTGGAGGAC" "CAGATGACATTCTC" "CAGCAATGGAGGGT" "CAGCGGACCTTTAC" ## [57] "CAGCTCTGTGTGGT" "CAGTTTACACACGT" "CATCAGGACTTCCG" "CATCAGGATAGCCA" ## [61] "CATGAGACGTTGAC" "CATTACACCAACTG" "CATTTCGAGATACC" "CCTCGAACACTTTC" ## [65] "CGACCACTAAAGTG" "CGACCACTGCCAAT" "CGAGGCTGACGCTA" "CGCCGAGAGCTTAG" ## [69] "CGGCGAACGACAAA" "CGGCGAACTACTTC" "CGGGCATGTCTCTA" "CGTACCTGGCATCA" ## [73] "CGTGTAGACGATAC" "CGTGTAGAGTTACG" "CGTGTAGATTCGGA" "CTAAACCTCTGACA" ## [77] "CTAACGGAACCGAT" "CTACGCACTGGTCA" "CTACTCCTATGTCG" "CTAGTTACGAAACA" ## [81] "CTATACTGCTACGA" "CTATACTGTCTCAT" "CTCGACTGGTTGAC" "CTGAGAACGTAAAG" ## [85] "CTTTAGTGACGGGA" "GAACCAACTTCCGC" "GAAGTGCTAAACGA" "GAATGCACCTTCGC" ## [89] "GAATTAACGTCGTA" "GACGGCACACGGGA" "GAGCGCTGAAGATG" "GAGGTACTGACACT" ## [93] "GAGGTGGATCCTCG" "GATAGAGAAGGGTG" "GATCCCTGACCTTT" "GCACACCTGTGCTA" ## [97] "GCACCACTTCCTTA" "GCACTAGAGTCGTA" "GCAGGGCTATCGAC" "GCCGGAACGTTCTT" ## [101] "GCCTACACAGTTCG" "GCGCATCTTGCTCC" "GCGCGATGGTGCAT" "GGAAGGTGGCGAGA" ## [105] "GGACGCTGTCCTCG" "GGAGGCCTCGTTGA" "GGCAAGGAAAAAGC" "GGCATATGCTTATC" ## [109] "GGCCGAACTCTAGG" "GGCTAAACACCTGA" "GGGTTAACGTGCAT" "GGTGGAGAAACGGG" ## [113] "GTAGTGTGAGCGGA" "GTCGACCTGAATGA" "GTGATTCTGGTTCA" "GTGTATCTAGTAGA" ## [117] "GTTAAAACCGAGAG" "GTTCAACTGGGACA" "GTTGACGATATCGG" "TAACTCACTCTACT" ## [121] "TAAGAGGACTTGTT" "TAATGCCTCGTCTC" "TACGGCCTGGGACA" "TACTACTGATGTCG" ## [125] "TACTCTGAATCGAC" "TACTGTTGAGGCGA" "TAGCATCTCAGCTA" "TAGCCCACAGCTAC" ## [129] "TAGGGACTGAACTC" "TAGTGGTGAAGTGA" "TAGTTAGAACCACA" "TATGAATGGAGGAC" ## [133] "TATGGGTGCATCAG" "TATTTCCTGGAGGT" "TCAACACTGTTTGG" "TCAGACGACGTTAG" ## [137] "TCCCGAACACAGTC" "TCCTAAACCGCATA" "TCGATTTGCAGCTA" "TCTAACACCAGTTG" ## [141] "TGATAAACTCCGTC" "TGCACAGACGACAT" "TGCCACTGCGATAC" "TGCTGAGAGAGCAG" ## [145] "TGGAACACAAACAG" "TGGTAGACCCTCAC" "TGTAATGACACAAC" "TGTAATGAGGTAAA" ## [149] "TTACTCGATCTACT" "TTAGTCTGCCAACA" "TTCCAAACTCCCAC" "TTCCCACTTGAGGG" ## [153] "TTCTAGTGGAGAGC" "TTCTGATGGAGACG" "TTGTCATGGACGGA"

# How can I extract expression matrix for all NK cells (perhaps, to load into another package) nk.raw.data 1) ## An object of class Seurat ## 13714 features across 414 samples within 1 assay ## Active assay: RNA (13714 features, 2000 variable features) ## 2 dimensional reductions calculated: pca, umap

subset(pbmc, subset = replicate == "rep2") ## An object of class Seurat ## 13714 features across 1290 samples within 1 assay ## Active assay: RNA (13714 features, 2000 variable features) ## 2 dimensional reductions calculated: pca, umap

# Can I create a Seurat object of just the NK cells and B cells? subset(pbmc, idents = c("NK", "B")) ## An object of class Seurat ## 13714 features across 499 samples within 1 assay ## Active assay: RNA (13714 features, 2000 variable features) ## 2 dimensional reductions calculated: pca, umap

# Can I create a Seurat object of all cells except the NK cells and B cells? subset(pbmc, idents = c("NK", "B"), invert = TRUE) ## An object of class Seurat ## 13714 features across 2139 samples within 1 assay ## Active assay: RNA (13714 features, 2000 variable features) ## 2 dimensional reductions calculated: pca, umap # note that if you wish to perform additional rounds of clustering after subsetting we recommend # re-running FindVariableFeatures() and ScaleData() 计算群内的平均基因表达

# How can I calculate the average expression of all cells within a cluster? cluster.averages

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# How can I calculate expression averages separately for each replicate? cluster.averages

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# You can also plot heatmaps of these 'in silico' bulk datasets to visualize agreement between # replicates DoHeatmap(cluster.averages, features = unlist(TopFeatures(pbmc[["pca"]], balanced = TRUE)), size = 3, draw.lines = FALSE)

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