🤩 scDiffCom | 看过来吧！~细胞间通讯怎么做差异分析呢！？

生信漫卷

发布于 2024-11-26 09:12:09

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文章被收录于专栏：R语言及实用科研软件R语言及实用科研软件

写在前面

scDiffCom包是一个用于单细胞数据中细胞间通讯分析的R工具包。😘

该工具包结合了最新的生物学知识库和单细胞表达数据，通过识别配体-受体相互作用来揭示细胞间的信号传递关系。💪

scDiffCom不仅提供了基于表达的相互作用预测，还考虑了配体、受体及其下游信号通路的活性和差异性，能够揭示在不同实验条件或细胞状态下细胞通讯的动态变化。🧐

用到的包

rm(list = ls())
# Install the development version from GitHub
#devtools::install_github("CyrilLagger/scDiffCom")
library(Seurat)
library(scDiffCom)
library(data.table)
library(tidyverse)

示例数据

seurat_object <- scDiffCom::seurat_sample_tms_liver
seurat_object

查看数据的metaata

细胞分类

table(seurat_object[["cell_type"]])

分组

# Cells can be grouped based on mice age
table(seurat_object[["age_group"]])

并行运算

如果你需要并行运算，提高速度的话，就加载这个包，然后设置用到的核心数。😘

这里数据量比较小，我们就不用了。😏

library(future)
plan(sequential) # sequentially in the current R process, equivalent to do nothing
#plan(multisession, workers = 4) # background R sessions
#plan(multicore, workers = 4) # forked R processes, not Windows/not RStudio

开始细胞间通讯差异分析

scdiffcom_object <- run_interaction_analysis(
  seurat_object = seurat_object,
  LRI_species = "mouse",
  seurat_celltype_id = "cell_type",
  seurat_condition_id = list(
    column_name = "age_group",
    cond1_name = "YOUNG",
    cond2_name = "OLD"
  )
)

scdiffcom_object

查看结果

# Retrieve and display all detected CCIs
CCI_detected <- GetTableCCI(scdiffcom_object, type = "detected", simplified = TRUE)

# Number of CCIs per regulation type (here with age)
table(CCI_detected$REGULATION)

# Retrieve the ORA results
ORA_results <- GetTableORA(scdiffcom_object, categories = "all", simplified = TRUE)

# Categories available
names(ORA_results)

火山图可视化

ggplot(CCI_detected, 
       aes(
         x = LOGFC,
         y = -log10(BH_P_VALUE_DE + 1E-2),
         colour = REGULATION
         )) +
  geom_point() + 
  theme_minimal()+
  scale_colour_manual(values = c("UP" = "#FB8072", "DOWN" = "#1965B0", "FLAT" = "#B2DF8A", "NSC" = "grey")) + 
  xlab("log(FC)") + 
  ylab("-log10(Adj. p-value)")

ORA富集分析结果可视化

# Plot the most over-represented up-regulated LRIs
PlotORA(object = scdiffcom_object,
        category = "LRI",
        regulation = "UP") + 
  theme_minimal()+
  theme(legend.position = 'right',
        legend.key.size = unit(0.4, "cm")
        )

# Plot the most over-represented down-regulated LRIs
PlotORA(object = scdiffcom_object,
        category = "LRI",
        regulation = "DOWN") + 
  theme_minimal()+
  theme(legend.position = 'right',
        legend.key.size = unit(0.4, "cm")
        )

网络可视化

if (!require("visNetwork")) install.packages("visNetwork")
if (!require("igraph")) install.packages("igraph")
if (!require("kableExtra")) install.packages("kableExtra")
if (!require("RColorBrewer")) install.packages("RColorBrewer")

BuildNetwork(
  object = scdiffcom_object
)

使用配体-受体相互作用的自定义数据库

scDiffcom还可以使用自己的LRI数据库和关联的注释。😘

这里我们以删减版mouse数据库为例。🫠

其实这里可以是任何数据库，只要它是data.table的列与默认值相同就行。😜

custom_LRI <- LRI_mouse$LRI_curated[1:100, ]

custom_LRI_GO <- LRI_mouse$LRI_curated_GO[LRI %in% custom_LRI$LRI]
custom_LRI_KEGG <- LRI_mouse$LRI_curated_KEGG[LRI %in% custom_LRI$LRI][, c("LRI", "KEGG_ID")]

scdiffcom_object_customlri <-  run_interaction_analysis(
  seurat_object = seurat_object,
  LRI_species = "custom",
  seurat_celltype_id = "cell_type",
  seurat_condition_id = list(
    column_name = "age_group",
    cond1_name = "YOUNG",
    cond2_name = "OLD"
  ),
  custom_LRI_tables = list(LRI = custom_LRI, custom_GO = custom_LRI_GO, custom_KEGG = custom_LRI_KEGG)
)

对自定义类别进行ORA分析

cell_families_relation <- data.table(
  EMITTER_CELLTYPE = c(
    "B cell",
    "T cell",
    "endothelial cell of hepatic sinusoid",
    "hepatocyte",
    "myeloid leukocyte"
  ),
  EMITTER_CELLFAMILY = c(
    "leukocyte",
    "leukocyte",
    "endothelial cell",
    "epithelial cell",
    "leukocyte"
  )
)

# Run ORA with the cell type families as extra annotation
scdiffcom_object <- RunORA(
  object = scdiffcom_object,
  extra_annotations = list(
    cell_families_relation
  ),
  overwrite = FALSE
)

最后祝大家早日不卷!~