写在前面：看官方文档，很可能会误认为很简单，或者有些人直接延用官方的路径。这都是不可取的，最重要的永远是自己的思路，不仅是科研方面的，还是代码绘图方面的。官方的绘图函数虽然比较漂亮，但是针对性很强，学习底层的绘图对个人意义才是最大的。官方的东西借鉴和学习。
有哪些组学呢？
有哪些单细胞多组学联用技术呢？
CITE-seq：Simultaneous epitope and transcriptome measurement in single cells | Nature Methods
10x multiome kit：Single Cell Multiome ATAC + Gene Expression - 10x Genomics
Cell Hashing：Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics | Genome Biology | Full Text (biomedcentral.com)
总之就是转录组+表面蛋白、ATAC、甲基化组、基因组。读文章再补充
多组学表面上是多层次，根本上可以解释为时间顺序上的故事，是可以互为因果的。
seurat这里有个Weighted Nearest Neighbors (WNN) 方法，根据多组学结合对细胞作聚类，有个加权过程。
Weighted Nearest Neighbor Analysis • Seurat (satijalab.org)
！但是这个文章并没有用WNN
1. 样本数据准备
1.1. 提取数据：8,617 cord blood mononuclear cells (CBMCs)
RNA和11个antibody-derived tags (ADT)表面蛋白
# 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
#检验永远是最重要哒
all.equal(colnames(cbmc.rna), colnames(cbmc.adt))
CollapseSpeciesExpressionMatrix(
object,
prefix = "HUMAN_",
controls = "MOUSE_",
ncontrols = 100
)
#这些小工具真的太有意思了
1.2. 创建seurat 对象
# 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) #seurat有很多query，可以queryseurat对象的某类数据，相当于seurat的子对象
## [1] "RNA"
Query Specific Object Types — Assays • SeuratObject (mojaveazure.github.io)
#assay子对象
# 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"
2. 根据scRNA-seq进行细胞聚类
# Note that all operations below are performed on the RNA assay Set and verify that the
# default assay is RNA
DefaultAssay(cbmc) <- "RNA"
紧接着就是一连串的标准流程，可以【包】seurat-1 回顾，下面代码有简单的注释
# perform visualization and clustering steps
cbmc <- NormalizeData(cbmc)#标准化数据
cbmc <- FindVariableFeatures(cbmc)#筛选高变异性基因
cbmc <- ScaleData(cbmc)#归一化高变异性基因
cbmc <- RunPCA(cbmc, verbose = FALSE)#线性分解细胞对高变异基因的差异解释度
cbmc <- FindNeighbors(cbmc, dims = 1:30)#细胞间距离
cbmc <- FindClusters(cbmc, resolution = 0.8, verbose = FALSE)#细胞聚类
cbmc <- RunUMAP(cbmc, dims = 1:30)#非线性展示低维细胞聚类
DimPlot(cbmc, label = TRUE)#绘图
标准化与归一化：标准化是去除样本间基线差异、归一化是去除参数间权重差异
聚类结果
3. 多组学切换，灵活可视化
# 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
# 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
feature图
4. markers鉴定
# 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")
小提琴图
# we can also identify alternative protein and RNA markers for this cluster through
# differential expression
adt_markers <- FindMarkers(cbmc, ident.1 = 6, assay = "ADT")
rna_markers <- FindMarkers(cbmc, ident.1 = 6, assay = "RNA")
head(adt_markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## CD19 2.067533e-215 1.2787751 1 1 2.687793e-214
## CD45RA 8.106076e-109 0.4117172 1 1 1.053790e-107
## CD4 1.123162e-107 -0.7255977 1 1 1.460110e-106
## CD14 7.212876e-106 -0.5060496 1 1 9.376739e-105
## CD3 1.639633e-87 -0.6565471 1 1 2.131523e-86
## CD8 1.042859e-17 -0.3001131 1 1 1.355716e-16
head(rna_markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## BANK1 0 1.963277 0.456 0.015 0
## CD19 0 1.563124 0.351 0.004 0
## CD22 0 1.503809 0.284 0.007 0
## CD79A 0 4.177162 0.965 0.045 0
## CD79B 0 3.774579 0.944 0.089 0
## FCRL1 0 1.188813 0.222 0.002 0
5. 其他绘图
# 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")
feature dot1
# view relationship between protein and RNA
FeatureScatter(cbmc, feature1 = "adt_CD3", feature2 = "rna_CD3E")
feature dot2
#对seurat数据格式要熟悉一点
# 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")
feature dot3
6. 小结
这个文章只是最基础的多组学联合分析，甚至都没涉及相关性计算。文章末尾也写了更多阅读的链接：
- Defining cellular identity from multimodal data using WNN analysis in Seurat v4 vignette
- Mapping scRNA-seq data onto CITE-seq references [vignette]
- Introduction to the analysis of spatial transcriptomics analysis [vignette] 空间好像也就是多了一个层次的信息
- Analysis of 10x multiome (paired scRNA-seq + ATAC) using WNN analysis [vignette]
- Signac: Analysis, interpretation, and exploration of single-cell chromatin datasets [package] 哪一层染色质信息呢？
- Mixscape: an analytical toolkit for pooled single-cell genetic screens [vignette]