Seurat4.0系列教程2：多模式数据联合分析

加载数据

能够同时测量来自同一细胞的多种数据类型，称为多模式联合分析，代表了单细胞基因组学的一个新的和令人兴奋的前沿。例如CITE-seq能够同时测量来自同一细胞的转录组和细胞表面蛋白质。其他令人兴奋的技术，如[10 XGenomics]，允许对 scRNA-seq和scATAC-seq进行配对测量。 Seurat 4，可以无缝存储、分析和探索多样化的多模式细胞数据集。

在这里，我们分析8，617个脐带血单核细胞（CBMCs）的数据集，其中转录组与11种表面蛋白质的丰度配对，对这些蛋白质的水平与DNA进行量化。
首先，我们加载两个计数矩阵：一个用于RNA测量，另一个用于抗体衍生标签（ADT）。您可以在此处下载ADT文件和RNA文件

library(Seurat)
library(ggplot2)
library(patchwork)
# Load in the RNA UMI matrix

# Note that this dataset also contains ~5% of mouse cells, which we can use as negative controls
# for the protein measurements. For this reason, the gene expression matrix has HUMAN_ or MOUSE_
# appended to the beginning of each gene.
cbmc.rna <- as.sparse(read.csv(file = "../data/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz", sep = ",", 
    header = TRUE, row.names = 1))

# To make life a bit easier going forward, we're going to discard all but the top 100 most
# highly expressed mouse genes, and remove the 'HUMAN_' from the CITE-seq prefix
cbmc.rna <- CollapseSpeciesExpressionMatrix(cbmc.rna)

# Load in the ADT UMI matrix
cbmc.adt <- as.sparse(read.csv(file = "../data/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz", sep = ",", 
    header = TRUE, row.names = 1))

# Note that since measurements were made in the same cells, the two matrices have identical
# column names
all.equal(colnames(cbmc.rna), colnames(cbmc.adt))
## [1] TRUE

设置seurat对象，添加RNA和蛋白质数据

现在，我们创建一个 Seurat 对象，并将 ADT 数据添加为第二个检测

# creates a Seurat object based on the scRNA-seq data
cbmc <- CreateSeuratObject(counts = cbmc.rna)

# We can see that by default, the cbmc object contains an assay storing RNA measurement
Assays(cbmc)

## [1] "RNA"

# create a new assay to store ADT information
adt_assay <- CreateAssayObject(counts = cbmc.adt)

# add this assay to the previously created Seurat object
cbmc[["ADT"]] <- adt_assay

# Validate that the object now contains multiple assays
Assays(cbmc)

## [1] "RNA" "ADT"

# Extract a list of features measured in the ADT assay
rownames(cbmc[["ADT"]])

##  [1] "CD3"    "CD4"    "CD8"    "CD45RA" "CD56"   "CD16"   "CD10"   "CD11c" 
##  [9] "CD14"   "CD19"   "CD34"   "CCR5"   "CCR7"

# Note that we can easily switch back and forth between the two assays to specify the default
# for visualization and analysis

# List the current default assay
DefaultAssay(cbmc)

## [1] "RNA"
# Switch the default to ADT
DefaultAssay(cbmc) <- "ADT"
DefaultAssay(cbmc)
## [1] "ADT"

基于 scRNA-seq 数据进行细胞聚类

下面的步骤表示基于 scRNA-seq 数据的 PBMC 的快速聚类。有关单个步骤或更高级选项的更多详细信息，请参阅此处的 PBMC 聚类引导教程

# Note that all operations below are performed on the RNA assay Set and verify that the default
# assay is RNA
DefaultAssay(cbmc) <- "RNA"
DefaultAssay(cbmc)
## [1] "RNA"
# perform visualization and clustering steps
cbmc <- NormalizeData(cbmc)
cbmc <- FindVariableFeatures(cbmc)
cbmc <- ScaleData(cbmc)
cbmc <- RunPCA(cbmc, verbose = FALSE)
cbmc <- FindNeighbors(cbmc, dims = 1:30)
cbmc <- FindClusters(cbmc, resolution = 0.8, verbose = FALSE)
cbmc <- RunUMAP(cbmc, dims = 1:30)
DimPlot(cbmc, label = TRUE)

image.png

并排可视化多模式数据

现在，我们已经从 scRNA-seq 文件中获得了聚类，我们可以在数据集中可视化蛋白质或RNA分子的表达。重要的是，Seurat 提供了在模式之间切换的几种方法，并指定您感兴趣的分析或可视化模式。这一点尤其重要，因为在某些情况下，相同的功能可能以多种方式存在 - 例如，此数据集包含 B 细胞标记 CD19（蛋白质和 RNA 水平）的独立测量。

# Normalize ADT data,
DefaultAssay(cbmc) <- "ADT"
cbmc <- NormalizeData(cbmc, normalization.method = "CLR", margin = 2)
DefaultAssay(cbmc) <- "RNA"

# Note that the following command is an alternative but returns the same result
cbmc <- NormalizeData(cbmc, normalization.method = "CLR", margin = 2, assay = "ADT")

# Now, we will visualize CD14 levels for RNA and protein By setting the default assay, we can
# visualize one or the other
DefaultAssay(cbmc) <- "ADT"
p1 <- FeaturePlot(cbmc, "CD19", cols = c("lightgrey", "darkgreen")) + ggtitle("CD19 protein")
DefaultAssay(cbmc) <- "RNA"
p2 <- FeaturePlot(cbmc, "CD19") + ggtitle("CD19 RNA")

# place plots side-by-side
p1 | p2

image.png

# Alternately, we can use specific assay keys to specify a specific modality Identify the key
# for the RNA and protein assays
Key(cbmc[["RNA"]])

## [1] "rna_"

Key(cbmc[["ADT"]])

## [1] "adt_"

# Now, we can include the key in the feature name, which overrides the default assay
p1 <- FeaturePlot(cbmc, "adt_CD19", cols = c("lightgrey", "darkgreen")) + ggtitle("CD19 protein")
p2 <- FeaturePlot(cbmc, "rna_CD19") + ggtitle("CD19 RNA")
p1 | p2

image

识别 scRNA-seq 亚群的细胞表面marker

我们可以利用我们的配对 CITE-seq 测量来帮助注释源自 scRNA-seq 的cluster，并识别蛋白质和RNA标记。

# as we know that CD19 is a B cell marker, we can identify cluster 6 as expressing CD19 on the
# surface
VlnPlot(cbmc, "adt_CD19")

image

# we can also identify alternative protein and RNA markers for this cluster through differential
# expression
adt_markers <- FindMarkers(cbmc, ident.1 = 5, assay = "ADT")
rna_markers <- FindMarkers(cbmc, ident.1 = 5, assay = "RNA")

head(adt_markers)

##                p_val avg_log2FC pct.1 pct.2     p_val_adj
## CD10   1.161293e-206  0.4512418     1     1 1.509680e-205
## CCR7   2.052649e-189  0.2835441     1     1 2.668443e-188
## CD34   9.647958e-188  0.4379917     1     1 1.254234e-186
## CCR5   4.601039e-150  0.2871257     1     1 5.981350e-149
## CD45RA  6.699498e-86 -2.2198583     1     1  8.709348e-85
## CD14    3.093576e-62 -0.7499958     1     1  4.021649e-61

head(rna_markers)

##               p_val avg_log2FC pct.1 pct.2 p_val_adj
## AC109351.1        0  0.3203893 0.265 0.005         0
## CTD-2090I13.1     0  2.0024376 0.972 0.062         0
## DCAF5             0  0.6637418 0.619 0.055         0
## DYNLL2            0  2.0387603 0.984 0.094         0
## FAM186B           0  0.3000479 0.244 0.002         0
## HIST2H2AB         0  1.3104432 0.812 0.013         0

多模式数据的其他可视化方法

# Draw ADT scatter plots (like biaxial plots for FACS). Note that you can even 'gate' cells if
# desired by using HoverLocator and FeatureLocator
FeatureScatter(cbmc, feature1 = "adt_CD19", feature2 = "adt_CD3")

image

# view relationship between protein and RNA
FeatureScatter(cbmc, feature1 = "adt_CD3", feature2 = "rna_CD3E")

image

FeatureScatter(cbmc, feature1 = "adt_CD4", feature2 = "adt_CD8")

image

# Let's look at the raw (non-normalized) ADT counts. You can see the values are quite high,
# particularly in comparison to RNA values. This is due to the significantly higher protein copy
# number in cells, which significantly reduces 'drop-out' in ADT data
FeatureScatter(cbmc, feature1 = "adt_CD4", feature2 = "adt_CD8", slot = "counts")

image

加载来自 10 xGenomics的多模式数据

Seurat 还能够分析使用 CellRanger v3 处理的多摸式10x实验的数据：例如，我们使用 7，900 个外周血单核细胞 （PBMC） 的数据集重新创建上述图，可从此处的 10X Genomics中免费获得。

pbmc10k.data <- Read10X(data.dir = "../data/pbmc10k/filtered_feature_bc_matrix/")
rownames(x = pbmc10k.data[["Antibody Capture"]]) <- gsub(pattern = "_[control_]*TotalSeqB", replacement = "", 
    x = rownames(x = pbmc10k.data[["Antibody Capture"]]))

pbmc10k <- CreateSeuratObject(counts = pbmc10k.data[["Gene Expression"]], min.cells = 3, min.features = 200)
pbmc10k <- NormalizeData(pbmc10k)
pbmc10k[["ADT"]] <- CreateAssayObject(pbmc10k.data[["Antibody Capture"]][, colnames(x = pbmc10k)])
pbmc10k <- NormalizeData(pbmc10k, assay = "ADT", normalization.method = "CLR")

plot1 <- FeatureScatter(pbmc10k, feature1 = "adt_CD19", feature2 = "adt_CD3", pt.size = 1)
plot2 <- FeatureScatter(pbmc10k, feature1 = "adt_CD4", feature2 = "adt_CD8a", pt.size = 1)
plot3 <- FeatureScatter(pbmc10k, feature1 = "adt_CD3", feature2 = "CD3E", pt.size = 1)
(plot1 + plot2 + plot3) & NoLegend()

image.png