10X单细胞(10X空间转录组)细胞通讯分析联合大全

作者,追风少年i

周四了,一周最好的时光,不知道大家感觉怎么样了?2022年上半年的最后一天,时光总是匆匆,不会为自己停留片刻。白哥有一句台词,之前做的再好也回不去了,项目交出去的那一刻,便不再属于自己,那如果新的项目做不成,就该考虑换个工作了~~~~你再有辉煌也都是过去的事情了,要是因为过去的事出不来的话,我们就会越来越平庸。

半年了,我们要做总结,希望大家都能在自己擅长的领域有所成就~~~

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七种方法的整合

show_methods()
#>  [1] "connectome"      "logfc"           "natmi"           "sca"            
#>  [5] "cellphonedb"     "cytotalk"        "call_squidpy"    "call_cellchat"  
#>  [9] "call_connectome" "call_sca"        "call_italk"      "call_natmi"

其中重点涉及

10X单细胞之细胞通讯篇章-----Connectome
10X单细胞(10X空间转录组)通讯分析之CellChat
考虑空间位置的通讯分析手段---CellphoneDB(V3.0)
10X单细胞(10X空间转录组)通讯分析之CytoTalk(从头构建信号转导网络 )
10X单细胞空间联合分析回顾之squidpy
10X单细胞(10X空间转录组)细胞通讯的一个小小的细节
单细胞数据细胞通讯分析软件NATMI
10X单细胞(10X空间转录组)通讯分析之NicheNet
数据库包括
# Resource currently included in OmniPathR (and hence `liana`) include:
show_resources()
#>  [1] "Default"          "Consensus"        "Baccin2019"       "CellCall"        
#>  [5] "CellChatDB"       "Cellinker"        "CellPhoneDB"      "CellTalkDB"      
#>  [9] "connectomeDB2020" "EMBRACE"          "Guide2Pharma"     "HPMR"            
#> [13] "ICELLNET"         "iTALK"            "Kirouac2010"      "LRdb"            
#> [17] "Ramilowski2015"   "OmniPath"

当然,我们要进一步扩充,首先来看LIANA与NicheNet的联合分析

LIANA(LIgand-receptor AAnalysis frAmework)是一个框架,能够使用不同的资源和方法对来自单细胞转录组学的配体-受体相互作用进行优先排序。 它允许用户系统地生成关于来自给定细胞类型的哪些配体与另一种细胞类型的受体结合的假设。 与 LIANA 相比,NicheNet 旨在深化将配体与一组转录靶标连接起来的细胞内机制,从而广泛使用来自多个来源的先验知识。 LIANA 和 NicheNet 并不相互排斥,但在某些情况下可能相当互补,因为它们旨在探索细胞间和细胞内通信的不同方面

图片.png

其中Nichenet的参考链接在NicheNet,值得大家深入学习。

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library(tidyverse)
library(liana)
library(nichenetr)
library(Seurat)
library(ggrepel)
library(cowplot)
options(timeout=300)

加载数据

# single-cell expression matrix described in Puram et al. 2017
hnscc_expression <- readRDS(url("https://zenodo.org/record/3260758/files/hnscc_expression.rds"))
# model weights
ligand_target_matrix <- readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))

感兴趣的细胞进行下游分析

expression <- hnscc_expression$expression
sample_info <- hnscc_expression$sample_info
colnames(sample_info) <- make.names(colnames(sample_info))

# filter samples based on vignette's information and add cell type
tumors_remove <-  c("HN10", "HN", "HN12", "HN13", "HN24", "HN7", "HN8", "HN23")
sample_info <- sample_info %>%
  subset( !(tumor %in% tumors_remove) & Lymph.node == 0) %>%
  # fix some cell type identity names
  mutate(cell_type = ifelse(classified..as.cancer.cell == 1, "Tumor", non.cancer.cell.type)) %>%
  subset(cell_type %in% c("Tumor", "CAF"))

# cell ID as rownames
rownames(sample_info) <- sample_info$cell

# subset expression to selected cells
expression <- expression[sample_info$cell, ]

# gene set of interest
geneset_oi <- read_tsv(url("https://zenodo.org/record/3260758/files/pemt_signature.txt"), col_types = cols(), col_names = "gene") %>%
  pull(gene) %>%
  .[. %in% rownames(ligand_target_matrix)]

Run LIANA

# create seurat object
seurat_object <- Seurat::CreateAssayObject(counts = expm1(t(expression))) %>%
  Seurat::CreateSeuratObject(., meta.data = sample_info) %>%
  Seurat::NormalizeData()

# set cell identity to cell type
Idents(seurat_object) <- [email protected]$cell_type

liana_results <- liana_wrap(seurat_object) %>%
  liana_aggregate()

By default, LIANA will score the ligand-receptor interactions in all the possible directions within the two cell types of interest. This includes: Autocrine signaling (e.g. CAFs -> CAFs), CAFs -> Tumor cells and Tumor cells -> CAFs. As we are only interested in the CAFs -> Tumor cell direction, we filter the results and visualize the top 50 interactions according to the consensus/aggregate rank across methods. The aggregate rank itself can be interpreted as the significance of preferential enrichment for the interactions.

# filter results to cell types of interest
caf_tumor_results <- liana_results %>%
  subset(source == "CAF" & target == "Tumor") %>%
  dplyr::rename(ligand=ligand.complex, receptor=receptor.complex)

# filter results to top N interactions
n <- 50
top_n_caf_tumor <- caf_tumor_results %>%
  arrange(aggregate_rank) %>%
  slice_head(n = n) %>%
  mutate(id = fct_inorder(paste0(ligand, " -> ", receptor)))

# visualize median rank
top_n_caf_tumor %>%
  ggplot(aes(y = aggregate_rank, x = id)) +
  geom_bar(stat = "identity") +
  xlab("Interaction") + ylab("LIANA's aggregate rank") +
  theme_cowplot() +
  theme(axis.text.x = element_text(size = 8, angle = 60, hjust = 1, vjust = 1))
图片.png

Run NicheNet using LIANA’s ligands

将 LIANA 与 NicheNet 结合的关键方面是可以使用 LIANA 优先考虑的配体作为 NicheNet 的潜在配体集。 不会评估受体也在受体细胞类型中表达的所有表达配体,我们将仅探索那些被 LIANA 中包含的方法优先考虑的配体。 因此,选择形成前面所示相互作用的配体。

# get ligands and filter to those included in NicheNet's ligand-target matrix
ligands <- unique(top_n_caf_tumor$ligand)
ligands <- ligands[ligands %in% colnames(ligand_target_matrix)]

background_genes <- expression[sample_info$cell[sample_info$cell_type == "Tumor"], ] %>%
  apply(2,function(x){10*(2**x - 1)}) %>%
  apply(2,function(x){log2(mean(x) + 1)}) %>%
  .[. >= 4] %>%
  names()

并使用前面提到的 pEMT 基因集执行 NicheNet 预测配体活动

nichenet_activities <- predict_ligand_activities(
  geneset = geneset_oi,
  background_expressed_genes = background_genes,
  ligand_target_matrix = ligand_target_matrix, potential_ligands = ligands
)

可视化

# prepare data for visualization
vis_liana_nichenet <- top_n_caf_tumor %>%
  inner_join(nichenet_activities, by = c("ligand" = "test_ligand")) %>%
  arrange(pearson) %>%
  mutate(ligand = fct_inorder(ligand))

# prepare NicheNet figure
nichenet_scores_plot <- vis_liana_nichenet %>%
  group_by(ligand) %>%
  summarize(pearson = mean(pearson)) %>%
  ggplot(aes(y = ligand, x = pearson)) +
  geom_bar(stat = "identity") +
  ggtitle("NicheNet") +
  xlab("Pearson's score") +
  theme_cowplot() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank(),
        axis.line.y = element_line(color = "white"),
        plot.title = element_text(hjust = 0.5),
        axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1))

# prepare LIANA figure
liana_receptor_heatmap <- vis_liana_nichenet %>%
  ggplot(aes(y = ligand, x = receptor, fill = aggregate_rank)) +
  geom_tile() +
  theme_cowplot() +
  ggtitle("LIANA") +
  ylab("Ligand") + xlab("Receptor") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
        plot.title = element_text(hjust = 0.5),
        panel.grid.major = element_line(colour = "gray", linetype = 2),
        legend.position = "left")

# combine plots
plot_grid(liana_receptor_heatmap, nichenet_scores_plot,
          align = "h", nrow = 1, rel_widths = c(0.8,0.3))
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其实,做单细胞通讯的软件非常多,我都放在下面,供大家参考,包括空间转录组做通讯的分析方法

10X空间转录组之构建邻域通讯网络

10X空间转录组之组织区域临近通讯实现方法

10X单细胞(10X空间转录组)细胞通讯分析之InterCellDB(推断通讯中感兴趣的生物学功能)

10X单细胞(10X空间转录组)数据分析之基于代谢物介导的细胞间通讯

10X空间转录组之空间临近通讯分析stlearn进阶版

10X单细胞(10X空间转录组)之单细胞精度分析细胞通讯

10X单细胞(10X空间转录组)通讯分析之汇总(scSeqComm)

10X空间转录组----空间临近通讯的实现方法

10X空间转录组数据分析之空间临近通讯网络(DAGBagST)

10X空间转录组之再谈距离分析和空间临近通讯

10X空间转录组的位点注释和临近通讯

10X单细胞(10X空间转录组)之配受体联合TF因子的通讯分析(cellcall)

10X单细胞(10X空间转录组)利用轨迹分析推断细胞间的实时通讯(TraSig)

10X单细胞(10X空间转录组)细胞通讯之组间通讯差异分析(scDiffCom)

10X单细胞(10X空间转录组)多种通讯软件联合分析之liana

10X单细胞(10X空间转录组)之细胞通讯软件之间的分析比较

10X单细胞(10X空间转录组)空间相关性分析和cellphoneDB与NicheNet联合进行细胞通讯分析

10X空间转录组细胞通讯之stlearn(寻找区域交流热点中心)

10X空间转录组(10X单细胞)之论细胞通讯空间分布的重要性

10X单细胞(10X空间转录组)通讯分析CellChat之多样本通讯差异分析

10X单细胞通讯分析之scMLnet(配受体与TF,差异基因(靶基因)网络通讯分析)

10X单细胞通讯分析之CrosstalkR(特异性和通讯强度的变化都很重要)

10X单细胞通讯分析之ICELLNET

10X空间转录组通讯分析章节3

空间通讯分析章节2

10X空间转录组做细胞通讯的打开方式

细胞通讯软件RNAMagnet

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