Seurat3.1的灵活操作指南

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下面演示了一些与Seurat对象进行交互的有用功能。出于演示目的，我们将使用在第一个指导教程中创建的2700 PBMC对象。您可以在此处下载预先计算的对象。为了模拟有两个重复的情况，将一半命名为“rep1”,另一半命名为”rep2”

官网3.1版本已经无法找到该指南的链接，其实还是有的，网址：
https://satijalab.org/seurat/v3.1/interaction_vignette.html

载入数据

library(Seurat)
pbmc <- readRDS(file = "../data/pbmc3k_final.rds")

### 随机设置两个重复
set.seed(42)
pbmc$replicate <- sample(c("rep1", "rep2"), size = ncol(pbmc), replace = TRUE)

从细胞聚类和样本重复中切换Idents

# 默认画的是object@ident)
DimPlot(pbmc, reduction = "umap")

# 把细胞分类因子先储存到对象中
pbmc$CellType <- Idents(pbmc)
# 切换Idents
Idents(pbmc) <- "replicate"
DimPlot(pbmc, reduction = "umap")

# 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) <- "CellType"

分别或同时统计不同聚类或者不同样本来源的细胞数目

# 每个聚类包含多少细胞？
table(Idents(pbmc))

## 
##  Naive CD4 T Memory CD4 T   CD14+ Mono            B        CD8 T 
##          697          483          480          344          271 
## FCGR3A+ Mono           NK           DC     Platelet 
##          162          155           32           14

# 每组重复包含多少细胞？
table(pbmc$replicate)

## 
## rep1 rep2 
## 1348 1290

# 每个聚类细胞数占比
prop.table(table(Idents(pbmc)))

## 
##  Naive CD4 T Memory CD4 T   CD14+ Mono            B        CD8 T 
##  0.264215315  0.183093252  0.181956027  0.130401820  0.102729340 
## FCGR3A+ Mono           NK           DC     Platelet 
##  0.061410159  0.058756634  0.012130402  0.005307051

# 样本分组和细胞聚类一起统计
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

提取特定的Seurat子集做亚型分析

# 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"

# 提取NK细胞的表达矩阵
nk.raw.data <- as.matrix(GetAssayData(pbmc, slot = "counts")[, WhichCells(pbmc, ident = "NK")])

# 获取基因表达量大于1的对象
subset(pbmc, subset = MS4A1 > 1)

## An object of class Seurat 
## 13714 features across 414 samples within 1 assay 
## Active assay: RNA (13714 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)
##  2 dimensional reductions calculated: pca, umap

# 选择两个细胞类型
subset(pbmc, idents = c("NK", "B"))

## An object of class Seurat 
## 13714 features across 499 samples within 1 assay 
## Active assay: RNA (13714 features)
##  2 dimensional reductions calculated: pca, umap

# 排除掉某些细胞类型
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)
##  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()

计算基因的平均表达量

# 计算平均表达量
cluster.averages <- AverageExpression(pbmc)
head(cluster.averages[["RNA"]][, 1:5])

	Native CD4 T	Memory CD4 T	CD14+Mono	B	CD8 T
AL627309.1	0.0061287	0.0059273	0.0485434	0.0000000	0.0205459
AP006222.2	0.0000000	0.0082061	0.0108847	0.0000000	0.0119149
RP11-206L10.2	0.0074531	0.0000000	0.0000000	0.0206503	0.0000000
RP11-206L10.9	0.0000000	0.0000000	0.0105012	0.0000000	0.0000000
LINC00115	0.0191189	0.0246905	0.0375374	0.0388854	0.0194828
NOC2L	0.4974632	0.3598115	0.2725375	0.5865349	0.5570490

# 返回Seurat对象用于下游分析
orig.levels <- levels(pbmc)
Idents(pbmc) <- gsub(pattern = " ", replacement = "_", x = Idents(pbmc))
orig.levels <- gsub(pattern = " ", replacement = "_", x = orig.levels)
levels(pbmc) <- orig.levels
cluster.averages <- AverageExpression(pbmc, return.seurat = TRUE)
cluster.averages

## An object of class Seurat 
## 13714 features across 9 samples within 1 assay 
## Active assay: RNA (13714 features)

# How can I plot the average expression of NK cells vs. CD8 T cells?  Pass do.hover = T for an
# interactive plot to identify gene outliers
CellScatter(cluster.averages, cell1 = "NK", cell2 = "CD8_T")

# How can I calculate expression averages separately for each replicate?
cluster.averages <- AverageExpression(pbmc, return.seurat = TRUE, add.ident = "replicate")
CellScatter(cluster.averages, cell1 = "CD8_T_rep1", cell2 = "CD8_T_rep2")

# 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)