单细胞转录组3大R包之Seurat

时间:2022-05-03
本文章向大家介绍单细胞转录组3大R包之Seurat，主要内容包括seurat的用法、进行一系列的QC步骤、normalization、rDetection of variable genes across the single cells、Scaling the data and removing unwanted sources of variation、PCA分析、找到有统计学显著性的主成分、Cluster the cells、基本概念、基础应用、原理机制和需要注意的事项等，并结合实例形式分析了其使用技巧，希望通过本文能帮助到大家理解应用这部分内容。

牛津大学的Rahul Satija等开发的Seurat，最早公布在Nature biotechnology, 2015，文章是； Spatial reconstruction of single-cell gene expression data , 在2017年进行了非常大的改动，所以重新在biorxiv发表了文章在 Integrated analysis of single cell transcriptomic data across conditions, technologies, and species 。 功能涵盖了scRNA-seq的QC、过滤、标准化、批次效应、PCA、tNSE亚群聚类分析、差异基因分析、亚群特异性标志物鉴定等等等。

其GitHub地址是：http://satijalab.org/seurat/

给初学者提供了一个2,700 PBMC scRNA-seq dataset from 10X genomics的数据实战指导，非常容易学会： http://satijalab.org/seurat/pbmc3k_tutorial.html 数据在： https://personal.broadinstitute.org/rahuls/seurat/seurat_files_nbt.zip

同时还提供两个公共数据的实战演练教程：

  • https://www.dropbox.com/s/4d00eyd84qscyd2/IntegratedAnalysis_Examples.zip?dl=1
  • http://bit.ly/IAexpmat

下载后如下所示：

IntegratedAnalysis_Examples
├── [ 211]  INSTALL
├── [1.4K]  README.md
├── [ 170]  data
│   ├── [ 83K]  Supplementary_Table_MarrowCellData.tsv
│   ├── [547K]  Supplementary_Table_PancreasCellData.tsv
│   └── [ 561]  regev_lab_cell_cycle_genes.txt
├── [ 170]  examples
│   ├── [2.9K]  marrow_commandList.R
│   └── [2.5K]  pancreas_commandList.R
└── [ 170]  tutorial
    ├── [8.2K]  Seurat_AlignmentTutorial.Rmd
    └── [7.6M]  Seurat_AlignmentTutorial.pdf
IntegratedAnalysis_ExpressionMatrices
├── [102M]  marrow_mars.expressionMatrix.txt
├── [ 66M]  marrow_ss2.expressionMatrix.txt
├── [330M]  pancreas_human.expressionMatrix.txt
├── [ 54M]  pancreas_mouse.expressionMatrix.txt
├── [165M]  pbmc_10X.expressionMatrix.txt
└── [101M]  pbmc_SeqWell.expressionMatrix.txt

seurat的用法

这里的测试数据是经由Illumina NextSeq 500测到的2,700 single cells 表达矩阵，下载地址；

根据表达矩阵构建seurat对象

需要准备好3个输入文件

library(Seurat)
library(dplyr)
library(Matrix)
## https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz 
## 下载整个压缩包解压即可重现整个流程
# Load the PBMC dataset
list.files("~/Downloads/filtered_gene_bc_matrices/hg19/")
## [1] "barcodes.tsv" "genes.tsv"    "matrix.mtx"
pbmc.data <- Read10X(data.dir = "~/Downloads/filtered_gene_bc_matrices/hg19/")

# Examine the memory savings between regular and sparse matrices
dense.size <- object.size(x = as.matrix(x = pbmc.data))
dense.size
## 709264728 bytes
sparse.size <- object.size(x = pbmc.data)
sparse.size
## 38715120 bytes
dense.size / sparse.size
## 18.3 bytes
# Initialize the Seurat object with the raw (non-normalized data).  Keep all
# genes expressed in >= 3 cells (~0.1% of the data). Keep all cells with at
# least 200 detected genes
pbmc <- CreateSeuratObject(raw.data = pbmc.data, min.cells = 3, min.genes = 200, 
    project = "10X_PBMC")
pbmc
## An object of class seurat in project 10X_PBMC 
##  13714 genes across 2700 samples.

进行一系列的QC步骤

mito.genes <- grep(pattern = "^MT-", x = rownames(x = pbmc@data), value = TRUE)
percent.mito <- Matrix::colSums(pbmc@raw.data[mito.genes, ]) / Matrix::colSums(pbmc@raw.data)

# AddMetaData adds columns to object@meta.data, and is a great place to stash QC stats
pbmc <- AddMetaData(object = pbmc, metadata = percent.mito, col.name = "percent.mito")
VlnPlot(object = pbmc, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3)
# GenePlot is typically used to visualize gene-gene relationships, but can be used for anything 
# calculated by the object, i.e. columns in object@meta.data, PC scores etc.
# Since there is a rare subset of cells with an outlier level of high mitochondrial percentage
# and also low UMI content, we filter these as well
par(mfrow = c(1, 2))
GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene")
# We filter out cells that have unique gene counts over 2,500 or less than 200
# Note that low.thresholds and high.thresholds are used to define a 'gate'
# -Inf and Inf should be used if you don't want a lower or upper threshold.
pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))

可以看到这里选择的QC标准是 200~2500基因范围内，以及线粒体基因表达占比小于5%的才保留。

normalization

这里默认根据细胞测序文库大小进行normalization，简单的做一个log转换即可。

 summary(pbmc@raw.data[,1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.1764  0.0000 76.0000
pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", 
    scale.factor = 10000)

summary(pbmc@data[,1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.1171  0.0000  5.7531

rDetection of variable genes across the single cells

pbmc <- FindVariableGenes(object = pbmc, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
length(x = pbmc@var.genes)
## [1] 1838

Scaling the data and removing unwanted sources of variation

需要去除那些technical noise,batch effects, or even biological sources of variation (cell cycle stage)

pbmc <- ScaleData(object = pbmc, vars.to.regress = c("nUMI", "percent.mito"))
summary(pbmc@scale.data[,1])

PCA分析

pbmc <- RunPCA(object = pbmc, pc.genes = pbmc@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5)
## [1] "PC1"
## [1] "CST3"   "TYROBP" "FCN1"   "LST1"   "AIF1"  
## [1] ""
## [1] "PTPRCAP" "IL32"    "LTB"     "CD2"     "CTSW"   
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
## [1] ""
## [1] "CD79A"    "MS4A1"    "HLA-DQA1" "TCL1A"    "HLA-DQB1"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "PF4"   "PPBP"  "SDPR"  "SPARC" "GNG11"
## [1] ""
## [1] "CYBA"     "HLA-DPA1" "HLA-DPB1" "HLA-DRB1" "CD37"    
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "IL32"   "GIMAP7" "AQP3"   "FYB"    "MAL"   
## [1] ""
## [1] "CD79A"    "HLA-DQA1" "CD79B"    "MS4A1"    "HLA-DQB1"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "FCER1A"  "LGALS2"  "MS4A6A"  "S100A8"  "CLEC10A"
## [1] ""
## [1] "FCGR3A"        "CTD-2006K23.1" "IFITM3"        "ABI3"         
## [5] "CEBPB"        
## [1] ""
## [1] ""

对PCA分析结果可以进行一系列的可视化： PrintPCA, VizPCA, PCAPlot, and PCHeatmap

# Examine and visualize PCA results a few different ways
PrintPCA(object = pbmc, pcs.print = 1:5, genes.print = 5, use.full = FALSE)
## [1] "PC1"
## [1] "CST3"   "TYROBP" "FCN1"   "LST1"   "AIF1"  
## [1] ""
## [1] "PTPRCAP" "IL32"    "LTB"     "CD2"     "CTSW"   
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
## [1] ""
## [1] "CD79A"    "MS4A1"    "HLA-DQA1" "TCL1A"    "HLA-DQB1"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "PF4"   "PPBP"  "SDPR"  "SPARC" "GNG11"
## [1] ""
## [1] "CYBA"     "HLA-DPA1" "HLA-DPB1" "HLA-DRB1" "CD37"    
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "IL32"   "GIMAP7" "AQP3"   "FYB"    "MAL"   
## [1] ""
## [1] "CD79A"    "HLA-DQA1" "CD79B"    "MS4A1"    "HLA-DQB1"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "FCER1A"  "LGALS2"  "MS4A6A"  "S100A8"  "CLEC10A"
## [1] ""
## [1] "FCGR3A"        "CTD-2006K23.1" "IFITM3"        "ABI3"         
## [5] "CEBPB"        
## [1] ""
## [1] ""
VizPCA(object = pbmc, pcs.use = 1:2)
PCAPlot(object = pbmc, dim.1 = 1, dim.2 = 2)
# ProjectPCA scores each gene in the dataset (including genes not included in the PCA) based on their correlation 
# with the calculated components. Though we don't use this further here, it can be used to identify markers that 
# are strongly correlated with cellular heterogeneity, but may not have passed through variable gene selection. 
# The results of the projected PCA can be explored by setting use.full=T in the functions above
pbmc <- ProjectPCA(object = pbmc, do.print = FALSE)

最重要的就是 PCHeatmap 函数了

PCHeatmap(object = pbmc, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)
PCHeatmap(object = pbmc, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, use.full = FALSE)

找到有统计学显著性的主成分

主成分分析结束后需要确定哪些主成分所代表的基因可以进入下游分析，这里可以使用JackStraw做重抽样分析。可以用JackStrawPlot可视化看看哪些主成分可以进行下游分析。

pbmc <- JackStraw(object = pbmc, num.replicate = 100, do.print = FALSE) 
JackStrawPlot(object = pbmc, PCs = 1:12)

当然，也可以用最经典的碎石图来确定主成分。

PCElbowPlot(object = pbmc)

这个确定主成分是非常有挑战性的: - The first is more supervised, exploring PCs to determine relevant sources of heterogeneity, and could be used in conjunction with GSEA for example. - The second implements a statistical test based on a random null model, but is time-consuming for large datasets, and may not return a clear PC cutoff. - The third is a heuristic that is commonly used, and can be calculated instantly.

在本例子里面，3种方法结果差异不大，可以在PC7~10直接挑选。

Cluster the cells

# save.SNN = T saves the SNN so that the clustering algorithm can be rerun using the same graph
# but with a different resolution value (see docs for full details)
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, resolution = 0.6, print.output = 0, save.SNN = TRUE)

A useful feature in Seurat v2.0 is the ability to recall the parameters that were used in the latest function calls for commonly used functions. For FindClusters, we provide the function PrintFindClustersParams to print a nicely formatted formatted summary of the parameters that were chosen.

PrintFindClustersParams(object = pbmc)
## Parameters used in latest FindClusters calculation run on: 2018-01-22 07:43:31
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function    Algorithm         n.start         n.iter
##      1                   1                 100             10
## -----------------------------------------------------------------------------
## Reduction used          k.param          k.scale          prune.SNN
##      pca                 30                25              0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10
# While we do provide function-specific printing functions, the more general function to 
# print calculation parameters is PrintCalcParams().

Run Non-linear dimensional reduction (tSNE)

同样也是一个函数，这个结果也可以像PCA分析一下挑选合适的PC进行下游分析。

pbmc <- RunTSNE(object = pbmc, dims.use = 1:10, do.fast = TRUE)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = pbmc)

这一步很耗时，可以保存该对象，便于重复，以及分享交流

save(pbmc, file = "pbmc3k.rData")

Finding differentially expressed genes (cluster biomarkers)

差异分析在seurat包里面被封装成了函数：FindMarkers，有一系列参数可以选择，然后又4种找差异基因的算法：

  • ROC test (“roc”)
  • t-test (“t”)
  • LRT test based on zero-inflated data (“bimod”, default)
  • LRT test based on tobit-censoring models (“tobit”)
# find all markers of cluster 1
cluster1.markers <- FindMarkers(object = pbmc, ident.1 = 1, min.pct = 0.25)
print(x = head(x = cluster1.markers, n = 5))
##                p_val avg_logFC pct.1 pct.2    p_val_adj
## S100A9  0.000000e+00  3.827593 0.996 0.216  0.00000e+00
## S100A8  0.000000e+00  3.786535 0.973 0.123  0.00000e+00
## LGALS2  0.000000e+00  2.634722 0.908 0.060  0.00000e+00
## FCN1    0.000000e+00  2.369524 0.956 0.150  0.00000e+00
## CD14   8.129864e-290  1.949317 0.663 0.029 1.11493e-285
# find all markers distinguishing cluster 5 from clusters 0 and 3
cluster5.markers <- FindMarkers(object = pbmc, ident.1 = 5, ident.2 = c(0,3), min.pct = 0.25)
print(x = head(x = cluster5.markers, n = 5))
##                p_val avg_logFC pct.1 pct.2     p_val_adj
## GZMB   3.854665e-190  3.195021 0.955 0.084 5.286288e-186
## IGFBP7 2.967797e-155  2.175917 0.542 0.010 4.070037e-151
## GNLY   7.492111e-155  3.514718 0.961 0.143 1.027468e-150
## FGFBP2 2.334109e-150  2.559484 0.852 0.085 3.200998e-146
## FCER1G 4.819154e-141  2.280724 0.839 0.100 6.608987e-137
# find markers for every cluster compared to all remaining cells, report only the positive ones
pbmc.markers <- FindAllMarkers(object = pbmc, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
pbmc.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)
## # A tibble: 16 x 7
## # Groups:   cluster [8]
##            p_val avg_logFC pct.1 pct.2     p_val_adj cluster     gene
##            <dbl>     <dbl> <dbl> <dbl>         <dbl>  <fctr>    <chr>
##  1 1.315805e-234  1.149058 0.924 0.483 1.804495e-230       0     LDHB
##  2 3.311687e-129  1.068122 0.662 0.202 4.541648e-125       0     IL7R
##  3  0.000000e+00  3.827593 0.996 0.216  0.000000e+00       1   S100A9
##  4  0.000000e+00  3.786535 0.973 0.123  0.000000e+00       1   S100A8
##  5  0.000000e+00  2.977399 0.936 0.042  0.000000e+00       2    CD79A
##  6 1.038405e-271  2.492236 0.624 0.022 1.424068e-267       2    TCL1A
##  7 8.029765e-207  2.158812 0.974 0.230 1.101202e-202       3     CCL5
##  8 1.118949e-181  2.113428 0.588 0.050 1.534527e-177       3     GZMK
##  9 1.066599e-173  2.275509 0.962 0.137 1.462733e-169       4   FCGR3A
## 10 1.996623e-123  2.151881 1.000 0.316 2.738169e-119       4     LST1
## 11 9.120707e-265  3.334634 0.955 0.068 1.250814e-260       5     GZMB
## 12 6.251673e-192  3.763928 0.961 0.131 8.573544e-188       5     GNLY
## 13 2.510362e-238  2.729243 0.844 0.011 3.442711e-234       6   FCER1A
## 14  7.037034e-21  1.965168 1.000 0.513  9.650588e-17       6 HLA-DPB1
## 15 2.592342e-186  4.952160 0.933 0.010 3.555138e-182       7      PF4
## 16 7.813553e-118  5.889503 1.000 0.023 1.071551e-113       7     PPBP

值得注意的是： The ROC test returns the ‘classification power’ for any individual marker (ranging from 0 - random, to 1 - perfect).

cluster1.markers <- FindMarkers(object = pbmc, ident.1 = 0, thresh.use = 0.25, test.use = "roc", only.pos = TRUE)

同时，该包提供了一系列可视化方法来检查差异分析的结果的可靠性：

  • VlnPlot (shows expression probability distributions across clusters)
  • FeaturePlot (visualizes gene expression on a tSNE or PCA plot) are our most commonly used visualizations
  • JoyPlot, CellPlot, and DotPlot 
VlnPlot(object = pbmc, features.plot = c("MS4A1", "CD79A"))
# you can plot raw UMI counts as well
VlnPlot(object = pbmc, features.plot = c("NKG7", "PF4"), use.raw = TRUE, y.log = TRUE)
FeaturePlot(object = pbmc, features.plot = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP", "CD8A"), cols.use = c("grey", "blue"), reduction.use = "tsne")

DoHeatmap generates an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.

pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_logFC) -> top10
# setting slim.col.label to TRUE will print just the cluster IDS instead of every cell name
DoHeatmap(object = pbmc, genes.use = top10$gene, slim.col.label = TRUE, remove.key = TRUE)

Assigning cell type identity to clusters

这个主要取决于生物学背景知识：

Cluster ID

Markers

Cell Type

0

IL7R

CD4 T cells

1

CD14, LYZ

CD14+ Monocytes

2

MS4A1

B cells

3

CD8A

CD8 T cells

4

FCGR3A, MS4A7

FCGR3A+ Monocytes

5

GNLY, NKG7

NK cells

6

FCER1A, CST3

Dendritic Cells

7

PPBP

Megakaryocytes

 
current.cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
new.cluster.ids <- c("CD4 T cells", "CD14+ Monocytes", "B cells", "CD8 T cells", "FCGR3A+ Monocytes", "NK cells", "Dendritic cells", "Megakaryocytes")
pbmc@ident <- plyr::mapvalues(x = pbmc@ident, from = current.cluster.ids, to = new.cluster.ids)
TSNEPlot(object = pbmc, do.label = TRUE, pt.size = 0.5)

Further subdivisions within cell types

# First lets stash our identities for later
pbmc <- StashIdent(object = pbmc, save.name = "ClusterNames_0.6")

# Note that if you set save.snn=T above, you don't need to recalculate the SNN, and can simply put: 
# pbmc <- FindClusters(pbmc,resolution = 0.8)
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, resolution = 0.8, print.output = FALSE)

# Demonstration of how to plot two tSNE plots side by side, and how to color points based on different criteria
plot1 <- TSNEPlot(object = pbmc, do.return = TRUE, no.legend = TRUE, do.label = TRUE)
plot2 <- TSNEPlot(object = pbmc, do.return = TRUE, group.by = "ClusterNames_0.6", no.legend = TRUE, do.label = TRUE)
plot_grid(plot1, plot2)
# Find discriminating markers
tcell.markers <- FindMarkers(object = pbmc, ident.1 = 0, ident.2 = 1)

# Most of the markers tend to be expressed in C1 (i.e. S100A4). However, we can see that CCR7 is upregulated in 
# C0, strongly indicating that we can differentiate memory from naive CD4 cells.
# cols.use demarcates the color palette from low to high expression
FeaturePlot(object = pbmc, features.plot = c("S100A4", "CCR7"), cols.use = c("green", "blue"))

The memory/naive split is bit weak, and we would probably benefit from looking at more cells to see if this becomes more convincing. In the meantime, we can restore our old cluster identities for downstream processing.

还有一个非常给力的用法，限于篇幅，就不介绍了，大家可以自行探索。

后面还有一个10X的单细胞实战，用的就是这个包，敬请期待。

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