--- title: 通过conda安装纯净环境的cellphoneDB tags: [] id: '1713' categories: - - 生信 - - 细胞通讯 date: 2022-04-20 19:48:28 --- [https://github.com/ventolab/CellphoneDB](https://github.com/ventolab/CellphoneDB) * conda create -n cpdb python=3.7 -y * conda activate cpdb * pip install cellphonedb * cellphonedb database list\_remote * cellphonedb database download * cellphonedb database list\_local * whereis cellphonedb * pip install markupsafe==2.0.1 * conda deactivate * rm -rf /opt/conda/pkgs/r-base-4.1.3-h06d3f91\_1 * conda create -n cpdb\_plot -c conda-forge r-base=4.1.3 -y * conda activate cpdb\_plot * conda install -c conda-forge r-pheatmap=1.0.12 -y * conda install -c conda-forge r-ggplot2=3.3.5 -y * whereis R ```r f_cpdb_prepare <- function(lc_scRNA, lc_dir, lc_className){ if (!file.exists(lc_dir)){dir.create(lc_dir)} # 生成 count.txt write.table(as.matrix(lc_scRNA@assays$RNA@data), file.path(lc_dir,'cellphonedb_count.txt'), sep='\t', quote=F) # 生成 meta.txt lc_meta_data <- cbind(rownames(lc_scRNA@meta.data), lc_scRNA@meta.data[, lc_className, drop=F]) lc_meta_data <- as.matrix(lc_meta_data) lc_meta_data[is.na(lc_meta_data)] = "Unkown" # 细胞类型中不能有NA write.table(lc_meta_data, file.path(lc_dir,'cellphonedb_meta.txt'), sep='\t', quote=F, row.names=F) } ``` ```r f_cpdb_run <- function(lc_dir, cpdb_Path = '/opt/conda/envs/cpdb/bin/cellphonedb', lc_R_HOME = '/opt/conda/envs/cpdb_plot/lib/R', threads=4){ tmp_w <- getwd() setwd(lc_dir) Sys.setenv(R_HOME = lc_R_HOME) print(Sys.getenv('R_HOME')) print(system(command = 'ls', intern = T)) print(system(command = paste0(cpdb_Path, ' database list_local'), intern = T)) print(system(command = paste0(cpdb_Path, ' method statistical_analysis cellphonedb_meta.txt cellphonedb_count.txt --counts-data=gene_name --threads=', threads), intern = T)) print(system(command = paste0(cpdb_Path, ' plot dot_plot'), intern = T)) print(system(command = paste0(cpdb_Path, ' plot heatmap_plot cellphonedb_meta.txt'), intern = T)) setwd(tmp_w) } ``` ```r # Rearrange data column sequence require(tidyverse) f_cDB_order_sequence <- function(lc_df){ da <- data.frame() df <- subset(lc_df, receptor_a == 'True' & receptor_b == 'False' receptor_a == 'False' & receptor_b == 'True') for(i in 1:length(df$gene_a)){ sub_data <- df[i, ] if(sub_data$receptor_b=='False'){ if(sub_data$receptor_a=='True'){ old_names <- colnames(sub_data) my_list <- strsplit(old_names[-c(1:11)], split="\\") my_character <- paste(sapply(my_list, '[[', 2L), sapply(my_list, '[[', 1L), sep='') new_names <- c(names(sub_data)[1:4], 'gene_b', 'gene_a', 'secreted', 'receptor_b', 'receptor_a', "annotation_strategy", "is_integrin", my_character) sub_data = dplyr::select(sub_data, new_names) # print('Change sequence!!!') names(sub_data) <- old_names da = rbind(da, sub_data) } }else{ da = rbind(da, sub_data) } } return(da) } f_cDB_mergePandM <- function(means_order, pvals_order){ means_sub <- means_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])] pvals_sub <- pvals_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])] means_gather <- tidyr::gather(means_sub, celltype, mean_expression, names(means_sub)[-1]) pvals_gather <- tidyr::gather(pvals_sub, celltype, pval, names(pvals_sub)[-1]) mean_pval <- dplyr::left_join(means_gather, pvals_gather, by = c('interacting_pair', 'celltype')) mean_pval } ``` ```r f_readcellphoneDB <- function(lc_dir){ res = list() res$pvals <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out","pvalues.txt"), check.names = FALSE)) res$means <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out", "means.txt"), check.names = FALSE)) res$s_means <- read.delim(file.path(lc_dir, "out", "significant_means.txt"), check.names = FALSE) res$m_p <- dplyr::distinct(f_cDB_mergePandM(res$means, res$pvals)) lc_tp <- res$m_p %>% dplyr::select(interacting_pair, celltype, pval) %>% tidyr::spread(key=celltype, value=pval) lc_sig_pairs <- lc_tp[which(rowSums(lc_tp<=0.05)!=0), ] res$s_m_p <- subset(res$m_p, interacting_pair %in% lc_sig_pairs$interacting_pair) res } ``` ```r f_cDB_dotplot <- function(lc_m_p){ lc_m_p %>% ggplot(aes(x=interacting_pair, y=celltype)) + # geom_point(aes(color=log2(mean_expression), size=pval)) + # scale_size(trans = 'reverse') + geom_point(aes(color=log2(mean_expression), size=-log10(pval+1*10^-3)) ) + guides(colour = guide_colourbar(order = 1),size = guide_legend(order = 2)) + labs(x='', y='') + scale_color_gradientn(name='Expression level \n(log2 mean expression \nmolecule1, molecule2)', colours = terrain.colors(100)) + # scale_color_gradient2('Expression level \n(log2 mean expression \nmolecule1, molecule2)', low = 'blue', mid = 'yellow', high = 'red') + theme(axis.text.x= element_text(angle=45, hjust=1)) + # coord_flip() + theme( panel.border = element_rect(color = 'black', fill = NA), panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank(), panel.background = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), axis.ticks = element_blank() # plot.title = element_text(hjust = 0.5), # legend.position = 'bottom' # guides(fill = guide_legend(label.position = "bottom")) # legend.position = "bottom" # axis.text.y.right = element_text(angle=270, hjust=0.5) ) + theme(legend.key.size = unit(0.4, 'cm'), #change legend key size # legend.key.height = unit(1, 'cm'), #change legend key height # legend.key.width = unit(1, 'cm'), #change legend key width legend.title = element_text(size=9), #change legend title font size legend.text = element_text(size=8)) #change legend text font size } ``` ## 示例 ```r library(Seurat) sce <- readRDS("~/upload/zl_liu//data//pca.rds") Myeloid <- subset(sce,cell_type=="Myeloid")[,1:100] f_cpdb_prepare(lc_scRNA = Myeloid, lc_dir = 'Myeloid_100', lc_className = 'cell_type_fig1spA') f_cpdb_run('Myeloid_100') br_d <- f_readcellphoneDB('Myeloid_100') tp_img <- f_cDB_dotplot(subset(br_d $s_m_p, pval<0.05)) tp_img ```