图是关于互作分析受配体的展示。横轴是source cell，纵轴是受配体对，展示了每个source与其他细胞的互作，也展示了每个组之间结果。其实看图就可以想到，用分面图就ok了，实际操作过程发现和我们之前的内容几乎一样(连夜更新---别说两组了，这个cellchat多组比较气泡图函数10组也能做了，连夜苦战---Cellphonedb v5受配体多组比较气泡图（原创函数）)。所以就不需要大家费劲的一步步操作，直接写成函数，当然了，还是依然考虑cellchat v2和cellphonedb v5两种方式。那么这两个方式可以整合到一个函数中嘛？可以呀，但我实在不行弄了，单个分开挺好的。此外，我们原图做了升级，可以设定分组和celltype颜色！
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（reference：Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord）
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object_list1 <- list(HD.cellchat, MDA.cellchat)
ks_CC_groupCells_bubble(object_list=object_list1,
group_name = c("HD","MDA"),
celltypes=c("Mon","Tcell" ,"Fibs", "ECs" ,"Mast" ),
LR_int = c("CCL14_ACKR1",
"CXCL2_ACKR1",
"CXCL12_CXCR4",
"ANXA1_FPR3",
"HLA-DRA_CD4",
"HLA-DQA1_CD4"),
cell_cols = c("#C1395E", "#AEC17B", "#E07B42", "#89A7C2", "#F0CA50"),
group_cols = c('#023858','#4d004b'))
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object_list2 <- list(HD.cellchat)
ks_CC_groupCells_bubble(object_list=object_list2,
group_name = c("HD"),
celltypes=c("Mon","Tcell" ,"Fibs", "ECs" ,"Mast" ),
LR_int = c("CCL14_ACKR1",
"CXCL2_ACKR1",
"CXCL12_CXCR4",
"ANXA1_FPR3",
"HLA-DRA_CD4",
"HLA-DQA1_CD4"),
cell_cols = c("#C1395E", "#AEC17B", "#E07B42", "#89A7C2", "#F0CA50"),
group_cols = c('white'))
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library(ggplot2)
library(tidyr)
#load data
GO_pvals <- read.delim("D:/KS项目/公众号文章/cellphonedb受配体多组比较气泡图函数/GO_cpdb/statistical_analysis_pvalues_08_15_2024_132104.txt", check.names = FALSE)
GO_means <- read.delim("D:/KS项目/公众号文章/cellphonedb受配体多组比较气泡图函数/GO_cpdb/statistical_analysis_means_08_15_2024_132104.txt", check.names = FALSE)
WT_pvals <- read.delim("D:/KS项目/公众号文章/cellphonedb受配体多组比较气泡图函数/WT_cpdb/statistical_analysis_pvalues_08_15_2024_132617.txt", check.names = FALSE)
WT_means <- read.delim("D:/KS项目/公众号文章/cellphonedb受配体多组比较气泡图函数/WT_cpdb/statistical_analysis_means_08_15_2024_132617.txt", check.names = FALSE)
data = list(list(pval=GO_pvals, means=GO_means),
list(pval=WT_pvals, means=WT_means))
#测试1：cpdb_anno没有pathway，用cpdb默认的，用通路选择
cpdb_interLR <- read.csv(file="D:/KS项目/公众号文章/cellphonedb受配体多组比较气泡图函数/cpdb_interLR.csv",header = T)
ks_cpdb_groupCells_bubble(cpdb_data = data,
group_name = c("GO","WT"),
cpdb_anno = cpdb_interLR,
celltypes = c("SMC","Mesenchymal","MuSCs","Myoblasts","Endothelial"),
LR_int = c("CDH2-CDH2",
"TGFB1-TGFBR3",
"DIO3+TG-THRA",
"FASLG-TNFRSF6B",
"VEGFA-FLT1",
"VEGFB-NRP1"),
cell_cols = c("#C1395E", "#AEC17B", "#E07B42", "#89A7C2", "#F0CA50"),
group_cols = c('#023858','#4d004b'))
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