Seurat 4.0 || 分析scRNA和膜蛋白数据

生信技能树jimmy

发布于 2020-11-09 12:25:44

1.9K0

发布于 2020-11-09 12:25:44

文章被收录于专栏：单细胞天地

作者 | 周运来

男，

一个长大了才会遇到的帅哥，

稳健，潇洒，大方，靠谱。

一段生信缘，一棵技能树。

生信技能树核心成员，单细胞天地特约撰稿人，简书创作者，单细胞数据科学家。

前情回顾

Seurat 4.0 ||您的单细胞数据分析工具箱上新啦

Seurat 4.0 ||单细胞多模态数据整合算法WNN

得益于单细胞多模态技术的发展，允许我们在单细胞水平从不同侧面考察细胞状态，如CITE-seq技术可以同时对单细胞转录组和膜蛋白进行定量。单细胞多模态数据分析面临的挑战和Seurat给出的解决方案，在预印本文章Integrated analysis of multimodal single-cell data中进行了介绍。算法落实到实现层面，我们来学习一下WNN的几篇教程，本文介绍了用于分析多模态单细胞数据集的加权最近邻(WNN)工作流程。该流程由三个步骤组成:

每个模态独立的预处理和降维
学习细胞特定的模态“权重”，并构建一个集成了这些模态的WNN图
WNN图的下游分析(如可视化、聚类等)

我们使用来自(Stuart， Butler等人，Cell 2019)的CITE-seq数据集，该数据集包含30,672个scRNA-seq 数据及25个抗体数据。研究对象包括RNA和抗体衍生标签(ADT)两种检测方法。

具体流程

要运行本教程，请安装Seurat v4，在github页面上有beta版本。

remotes::install_github("satijalab/seurat", ref = "release/4.0.0")

如下载入我们的老朋友：

library(Seurat)
library(SeuratData)
library(cowplot)
library(dplyr)

下载示例数据集

InstallData("bmcite")
bm <- LoadData(ds = "bmcite")

照例我们看看数据是怎么样的：

bm
An object of class Seurat 
17034 features across 30672 samples within 2 assays 
Active assay: RNA (17009 features, 2000 variable features)
 1 other assay present: ADT
 1 dimensional reduction calculated: spca

我们看到这个Seurat对象有两个 assays每个 assays 都有相应的表达谱。

dim(bm@assays$RNA)
[1] 17009 30672

bm@assays$ADT@counts[1:4,1:4]
4 x 4 sparse Matrix of class "dgCMatrix"
            a_AAACCTGAGCTTATCG-1 a_AAACCTGAGGTGGGTT-1 a_AAACCTGAGTACATGA-1 a_AAACCTGCAAACCTAC-1
CD11a                        110                  541                  120                  645
CD11c                         51                   12                   10                   42
CD123                         14                    5                    1                    5
CD127-IL7Ra                   20                  187                   43                  115

RNA的assay我们是很熟悉了，ADT数据也是一个表达谱，包含的抗体：

rownames(bm@assays$ADT)
 [1] "CD11a"       "CD11c"       "CD123"       "CD127-IL7Ra" "CD14"        "CD16"        "CD161"       "CD19"        "CD197-CCR7"  "CD25"        "CD27"       
[12] "CD278-ICOS"  "CD28"        "CD3"         "CD34"        "CD38"        "CD4"         "CD45RA"      "CD45RO"      "CD56"        "CD57"        "CD69"       
[23] "CD79b"       "CD8a"        "HLA.DR"

我们首先分别对这两种数据进行预处理和降维。我们使用标准的标准化，但您也可以使用SCTransform或任何替代方法。

DefaultAssay(bm) <- 'RNA'
bm <- NormalizeData(bm) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA()

DefaultAssay(bm) <- 'ADT'
# we will use all ADT features for dimensional reduction
# we set a dimensional reduction name to avoid overwriting the 
VariableFeatures(bm) <- rownames(bm[["ADT"]])
bm <- NormalizeData(bm, normalization.method = 'CLR', margin = 2) %>% 
  ScaleData() %>% RunPCA(reduction.name = 'apca')

Normalizing across cells
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  
Centering and scaling data matrix
  |================================================================================================================================================================| 100%
Warning in irlba(A = t(x = object), nv = npcs, ...) :
  You're computing too large a percentage of total singular values, use a standard svd instead.
Warning in irlba(A = t(x = object), nv = npcs, ...) :
  did not converge--results might be invalid!; try increasing work or maxit
PC_ 1 
Positive:  CD3, CD28, CD27, CD127-IL7Ra, CD278-ICOS, CD4, CD8a, CD161, CD25, CD45RO 
       CD45RA, CD197-CCR7 
Negative:  HLA.DR, CD11c, CD14, CD38, CD69, CD123, CD11a, CD34, CD19, CD79b 
       CD16, CD56 
PC_ 2 
Positive:  CD45RO, CD11a, CD14, CD4, CD11c, CD28, CD3, CD69, CD278-ICOS, CD127-IL7Ra 
       CD38, CD161 
Negative:  CD45RA, CD19, CD79b, CD197-CCR7, CD8a, CD57, CD56, CD16, CD27, CD25 
       HLA.DR, CD34 
PC_ 3 
Positive:  CD16, CD56, CD8a, CD11a, CD57, CD161, CD45RA, CD38, CD3, CD127-IL7Ra 
       CD27, CD11c 
Negative:  CD19, CD79b, CD4, CD25, HLA.DR, CD197-CCR7, CD69, CD34, CD28, CD45RO 
       CD14, CD278-ICOS 
PC_ 4 
Positive:  CD161, CD25, CD45RO, CD56, CD16, CD4, CD79b, CD19, CD28, CD57 
       CD127-IL7Ra, HLA.DR 
Negative:  CD8a, CD27, CD14, CD45RA, CD3, CD11c, CD69, CD38, CD197-CCR7, CD11a 
       CD34, CD123 
PC_ 5 
Positive:  CD4, CD38, CD123, CD16, CD34, CD45RA, CD56, CD278-ICOS, CD28, CD197-CCR7 
       CD27, CD3 
Negative:  CD45RO, CD8a, CD69, CD161, CD79b, CD19, CD25, CD57, CD14, CD11a 
       CD127-IL7Ra, HLA.DR 
Warning messages:
1: Requested number is larger than the number of available items (25). Setting to 25. 
2: Requested number is larger than the number of available items (25). Setting to 25. 
3: Requested number is larger than the number of available items (25). Setting to 25. 
4: Requested number is larger than the number of available items (25). Setting to 25. 
5: Requested number is larger than the number of available items (25). Setting to 25. 
6: Cannot add objects with duplicate keys (offending key: PC_), setting key to 'apca_'

随着单细胞多模态技术的发展，我们要习惯一个对象有多个assay的数据结构。数据结构就像一处盖好的大楼，房间都在那了，就是往数据里面填我们计算好的数据，比如这里的apca。

head(bm@reductions$apca@cell.embeddings)
                        apca_1     apca_2     apca_3     apca_4     apca_5      apca_6     apca_7      apca_8      apca_9    apca_10     apca_11    apca_12     apca_13
a_AAACCTGAGCTTATCG-1 -1.546214 -1.0560783 -0.3193425  0.5561288  1.1332821 -3.46190907 -0.6363978 -0.69711363 -0.15623767 -0.1291178  0.43826804  0.4848001  0.02257897
a_AAACCTGAGGTGGGTT-1  2.698538  1.5314222  2.2152152  3.8489785 -0.2302644  0.41040618  1.7093026 -2.08814571  1.93091407  1.2356946  0.21717667 -0.6694691 -1.46231202
a_AAACCTGAGTACATGA-1  3.287854  0.2591767  0.1191195 -0.7199622  1.7688191  0.01789973 -1.2586785  0.08800729  0.42980044  0.9671439 -0.73346613 -0.3946358 -1.28168949
a_AAACCTGCAAACCTAC-1  3.100410  2.3937004 -0.9758685  1.0520679  0.0893280  0.01760703 -0.4626806  0.44875816  0.70985188  0.6547350 -0.12642028  0.2164491  1.06710170
a_AAACCTGCAAGGTGTG-1 -3.707086  2.5657331 -0.3487033 -0.6951109 -0.7601989  0.20388700 -0.7173617 -0.12882660 -0.08661708  0.3569946 -0.10704170 -0.5074276 -0.07922931
a_AAACCTGCACGGTAGA-1 -2.627561 -3.7290142 -2.2912037  0.5042828 -0.3032089  0.12050431  0.3047013 -0.01124192  0.74586469 -0.4926128  0.04005597  0.3078714  0.62619888
                         apca_14      apca_15     apca_16     apca_17    apca_18     apca_19    apca_20     apca_21     apca_22     apca_23     apca_24
a_AAACCTGAGCTTATCG-1  0.03922873  0.284971458 -0.05987775 -0.63780150 -0.3893929  0.72669647  0.4473322  0.30682227 -0.56594325 -0.14519199  0.26113206
a_AAACCTGAGGTGGGTT-1 -1.26101465  0.727477850 -1.33392808  0.06360178 -0.2847066  0.47726469 -0.2159961 -0.75962725  0.31336909  0.27562030 -0.46558363
a_AAACCTGAGTACATGA-1 -0.09693877 -0.060252586  0.21063702 -0.31319328  0.2271467  0.17773498  0.3805679  0.28309195  0.15551460 -0.01404676 -0.12147257
a_AAACCTGCAAACCTAC-1  0.09764741 -0.001231692  0.34339056 -0.05304418  0.1969659  0.05418865 -0.2202433 -0.16040501 -0.12525310  0.05894026 -0.03718946
a_AAACCTGCAAGGTGTG-1  0.24172883  0.040471346  0.24044018  0.34446924 -0.1282743 -0.11257312 -0.2512336  0.18092572  0.23236869 -0.10883569  0.05730309
a_AAACCTGCACGGTAGA-1 -0.18285025  0.911541345  0.20308131 -0.81552469 -0.5860654  0.25639653  0.3117227 -0.06062992 -0.07510367 -0.29029817 -0.01214964

对于每个细胞，我们根据RNA和蛋白质相似度的加权组合来计算它在数据集中最近的邻居。细胞特有的模态权重和多模态邻居在一个函数中计算，该函数在此数据集上运行约2分钟。我们指定每个模态的维数(类似于指定要包含在scRNA-seq集群中的pc的数量)，但是您可以改变这些设置，以看到小的更改对总体结果的影响最小。这是Seurat 4.0 最大的一个更新，一个函数整合多模态数据。所谓的模态在我们数据分析中就是多了一个表格与同模态不同的是，这里的数据来源不同。在使用之前我们先看一下这个函数的文档?FindMultiModalNeighbors。

?FindMultiModalNeighbors
# Identify multimodal neighbors. These will be stored in the neighbors slot, 
# and can be accessed using bm[['weighted.nn']]
# The WNN graph can be accessed at bm[["wknn"]], 
# and the SNN graph used for clustering at bm[["wsnn"]]
# Cell-specific modality weights can be accessed at bm$RNA.weight
bm <- FindMultiModalNeighbors(
  bm, reduction.list = list("pca", "apca"), 
  dims.list = list(1:30, 1:18), modality.weight.name = "RNA.weight"
)

可以看出整合多模态是在各自的降维空间里进行的，可以指定降维方法和维度。

Calculating cell-specific modality weights
Finding 20 nearest neighbors for each modality.
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s  
Calculating kernel bandwidths
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  
Finding multimodal neighbors
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 07s
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Constructing multimodal KNN graph
Constructing multimodal SNN graph

我们来看看这个加权矩阵有哪些内容。

str(bm@neighbors$weighted.nn)
Formal class 'Neighbor' [package "Seurat"] with 5 slots
  ..@ nn.idx    : num [1:30672, 1:20] 21408 26747 2354 14276 2344 ...
  ..@ nn.dist   : num [1:30672, 1:20] 0.146 0.202 0.297 0.336 0.301 ...
  ..@ alg.idx   : NULL
  ..@ alg.info  : list()
  ..@ cell.names: chr [1:30672] "a_AAACCTGAGCTTATCG-1" "a_AAACCTGAGGTGGGTT-1" "a_AAACCTGAGTACATGA-1" "a_AAACCTGCAAACCTAC-1" ...

head(bm@neighbors$weighted.nn@nn.idx)
      [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 21408 24052  3638 22480 24855 21388 12916 21993 15870  2248 20577 15011 27793 16535 12400 29694 30082  8634 15448 20145
[2,] 26747  7037  3857 10338  8055 25281 14106 28950 10337  9524 17717 12946 29411  7192   638 14297 27010 17611  7282  8051
[3,]  2354  9333  3422  8414 22000 30077   993 29367 19518  8283 20360  4269 17634  8542  9665  8855 12444  3587 14110  1120
[4,] 14276 28818 12742 11694 27019 16333   809 24540 27160 22614 28896  3774  1841 18030  5055 25481 23135 11424 22303 21478
[5,]  2344  7799  6268 12324 13830  4142  7393 10436 12439 27303 19328 25339 24713  5597 14001 20544 19067 12394  4108 28421
[6,]  5797 21480 12600 24915 21105  2197 18431 23934 22306 19231 11036  1687 24800 23613 24455 19422 14841 17582 29698 28724

WNN图结构被存在bm[["wknn"]]，用于聚类的SNN图在bm[["wsnn"]]，既然是图，我们不免想起要用igraph探索一番。以bm[["wknn"]]为例。

library(igraph)
net <- graph_from_adjacency_matrix(bm[["wknn"]]) 
net

net
IGRAPH 2231c24 DN-- 30672 1014296 -- 
+ attr: name (v/c)
+ edges from 2231c24 (vertex names):
 [1] a_AAACCTGAGCTTATCG-1->a_AAACCTGAGCTTATCG-1 a_AGGCCACTCTTCATGT-1->a_AAACCTGAGCTTATCG-1 a_CACAGTAAGTATTGGA-1->a_AAACCTGAGCTTATCG-1
 [4] a_CTGTTTACACCTCGGA-1->a_AAACCTGAGCTTATCG-1 a_GATGCTAGTCAATGTC-1->a_AAACCTGAGCTTATCG-1 a_TCGGGACGTGACTACT-1->a_AAACCTGAGCTTATCG-1
+ ... omitted several edges

这是一个有向图，节点数是30672 （细胞数），边数量为1014296 。

is_weighted(net)
[1] FALSE
 is_simple(net)
[1] FALSE
 edge_density(net)
[1] 0.001078188
transitivity(net)
[1] 0.1941536
 reciprocity(net, mode="default")
[1] 1
 is_connected(net)
[1] TRUE
comps <- decompose(net)
 table(sapply(comps, vcount))

30672 
    1

边的分布：

d.net <- degree(net)
hist(d.net,col="blue",
     xlab="Degree", ylab="Frequency",
     main="Degree Distribution")

取子图，主要是这么多节点不好画图。

par(mfrow=c(1, 2))
plot(subnet)
visualize_graph( subnet, centrality.type="Markov Centrality")

可以看出有的细胞有许多链接，但是大部分细胞和其他的没有什么链接。

然后，我们基于这个网络，来用igraph的cluster_fast_greedy聚类。

# igraph::as.undirected()
kc <- cluster_fast_greedy( as.undirected(net)) # 
sizes(kc)

Community sizes
   1    2    3    4    5    6    7    8 
  76  377  370 9532   23 3997 8088 8209

聚成了8个类，看每个细胞的归宿。

head(membership(kc))# 社团归宿 )
a_AAACCTGAGCTTATCG-1 a_AAACCTGAGGTGGGTT-1 a_AAACCTGAGTACATGA-1 a_AAACCTGCAAACCTAC-1 a_AAACCTGCAAGGTGTG-1 a_AAACCTGCACGGTAGA-1 
                   4                    8                    7                    7                    4                    6

对聚类结果做个可视化

library(ape)
dendPlot(kc, mode="phylo")

细胞太多了

好了，结束我们对数据结构的探索。这不是教程的一部分，而是我们学了网络图之后，探究起细胞组成的网络是可以有许多自己的视角。我们现在可以使用这些结果进行下游分析，比如可视化和聚类。例如，我们可以基于RNA和蛋白质数据的加权组合创建数据的UMAP可视化。我们还可以执行基于图形的聚类，并在UMAP上可视化这些结果，以及一组细胞注释。


bm <- RunUMAP(bm, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_")
bm <- FindClusters(bm, graph.name = "wsnn", algorithm = 3, resolution =seq( 0,2,0.4), verbose = FALSE)

head(bm@meta.data)
                     orig.ident nCount_RNA nFeature_RNA nCount_ADT nFeature_ADT      lane  donor      celltype.l1 celltype.l2 RNA.weight wsnn_res.2
a_AAACCTGAGCTTATCG-1     bmcite       7546         2136       1350           25 HumanHTO4 batch1 Progenitor cells    Prog_RBC  0.4827011         19
a_AAACCTGAGGTGGGTT-1     bmcite       1029          437       2970           25 HumanHTO1 batch1           T cell         gdT  0.2417890         10
a_AAACCTGAGTACATGA-1     bmcite       1111          429       2474           23 HumanHTO5 batch1           T cell   CD4 Naive  0.5077136          1
a_AAACCTGCAAACCTAC-1     bmcite       2741          851       4799           25 HumanHTO3 batch1           T cell  CD4 Memory  0.4313079          4
a_AAACCTGCAAGGTGTG-1     bmcite       2099          843       5434           25 HumanHTO2 batch1          Mono/DC   CD14 Mono  0.5685085          2
a_AAACCTGCACGGTAGA-1     bmcite       2291          783       4658           25 HumanHTO6 batch1           B cell     Naive B  0.4255879          5
                     seurat_clusters wsnn_res.0 wsnn_res.0.4 wsnn_res.0.8 wsnn_res.1.2 wsnn_res.1.6
a_AAACCTGAGCTTATCG-1              19          0            9            8           18           19
a_AAACCTGAGGTGGGTT-1              10          0           10            9            9            9
a_AAACCTGAGTACATGA-1               1          0            1            1            0            1
a_AAACCTGCAAACCTAC-1               4          0            3            3            4            4
a_AAACCTGCAAGGTGTG-1               2          0            0            0            1            2
a_AAACCTGCACGGTAGA-1               5          0            4            4            5            5

library(clustree)
clustree(bm,prefix = 'wsnn_res.')

p1 <- DimPlot(bm, reduction = 'wnn.umap', label = TRUE, repel = TRUE, label.size = 2.5) + NoLegend()
p2 <- DimPlot(bm, reduction = 'wnn.umap', group.by = 'celltype.l2', label = TRUE, repel = TRUE, label.size = 2.5) + NoLegend()
p1 + p2

我们也可以计算UMAP可视化仅基于RNA和蛋白质数据和比较。我们发现，在识别祖细胞状态方面，RNA分析比ADT分析提供的信息更多(ADT panel包含分化细胞的标记)，而T细胞状态则相反(ADT分析优于RNA分析)。

bm <- RunUMAP(bm, reduction = 'pca', dims = 1:30, assay = 'RNA', 
              reduction.name = 'rna.umap', reduction.key = 'rnaUMAP_')
bm <- RunUMAP(bm, reduction = 'apca', dims = 1:18, assay = 'ADT', 
              reduction.name = 'adt.umap', reduction.key = 'adtUMAP_') # 这个dims选择的有技术含量啊

p3 <- DimPlot(bm, reduction = 'rna.umap', group.by = 'celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5) + NoLegend()
p4 <- DimPlot(bm, reduction = 'adt.umap', group.by = 'celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5) + NoLegend()
p3 + p4

我们可以可视化多模态UMAP上典型标记基因和蛋白的表达，这有助于验证所提供的注释:

p5 <- FeaturePlot(bm, features = c("adt_CD45RA","adt_CD16","adt_CD161"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)
p6 <- FeaturePlot(bm, features = c("rna_TRDC","rna_MPO","rna_AVP"), 
                  reduction = 'wnn.umap', max.cutoff = 3, ncol = 3)
p5 / p6

最后，我们可以将每个细胞的模态权重可视化。RNA重量最高的每个群体代表祖细胞，而蛋白重量最高的群体代表T细胞。这符合我们的生物学预期，因为抗体panel不包含可以区分不同祖细胞群体的标记。

VlnPlot(bm, features = "RNA.weight", group.by = 'celltype.l2', sort = TRUE, pt.size = 0) +
  NoLegend()

References

[1] https://satijalab.org/seurat/v4.0/weighted_nearest_neighbor_analysis.html: http

本文参与腾讯云自媒体同步曝光计划，分享自微信公众号。

原始发表：2020-11-03，如有侵权请联系 cloudcommunity@tencent.com 删除

数据结构

数据分析

本文分享自单细胞天地微信公众号，前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

本文参与腾讯云自媒体同步曝光计划，欢迎热爱写作的你一起参与！

数据结构

数据分析

登录后参与评论

0 条评论

热度