# ============================================================================= # Cell-Cell Communication Analysis — Export Results # ============================================================================= # Exports all CellChat analysis results: CSV tables, CellChat RDS object, # markdown report, and PDF report (via generate_report.R). # ============================================================================= suppressPackageStartupMessages({ library(CellChat) }) #' Export all CellChat analysis results #' #' @param cellchat CellChat object (from run_cellchat_analysis) #' @param seurat_obj Original Seurat object (optional, for metadata export) #' @param output_dir Output directory export_all <- function(cellchat, seurat_obj = NULL, output_dir = "results") { cat("\n=== Exporting Results ===\n\n") dir.create(output_dir, showWarnings = FALSE, recursive = TRUE) # ------------------------------------------------------------------------- # 1. Significant interactions (all L-R pairs with statistics) # ------------------------------------------------------------------------- cat("1. Significant interactions...\n") df_net <- subsetCommunication(cellchat) write.csv(df_net, file.path(output_dir, "significant_interactions.csv"), row.names = FALSE) cat(" Saved:", file.path(output_dir, "significant_interactions.csv"), "(", nrow(df_net), "interactions )\n") # ------------------------------------------------------------------------- # 2. Pathway-level summary # ------------------------------------------------------------------------- cat("2. Pathway-level summary...\n") df_pathway <- subsetCommunication(cellchat, slot.name = "netP") write.csv(df_pathway, file.path(output_dir, "pathway_summary.csv"), row.names = FALSE) cat(" Saved:", file.path(output_dir, "pathway_summary.csv"), "(", nrow(df_pathway), "pathway-level interactions )\n") # ------------------------------------------------------------------------- # 3. Interaction count matrix (cell type × cell type) # ------------------------------------------------------------------------- cat("3. Interaction count matrix...\n") count_mat <- cellchat@net$count write.csv(count_mat, file.path(output_dir, "interaction_count_matrix.csv")) cat(" Saved:", file.path(output_dir, "interaction_count_matrix.csv"), "\n") # ------------------------------------------------------------------------- # 4. Interaction strength matrix (cell type × cell type) # ------------------------------------------------------------------------- cat("4. Interaction strength matrix...\n") weight_mat <- cellchat@net$weight write.csv(weight_mat, file.path(output_dir, "interaction_strength_matrix.csv")) cat(" Saved:", file.path(output_dir, "interaction_strength_matrix.csv"), "\n") # ------------------------------------------------------------------------- # 5. Signaling roles (centrality scores) # ------------------------------------------------------------------------- cat("5. Signaling role scores...\n") tryCatch({ # Extract centrality measures for each pathway centr_list <- list() pathways <- cellchat@netP$pathways for (pw in pathways) { pw_centr <- tryCatch( cellchat@netP$centr[[pw]], error = function(e) NULL ) if (!is.null(pw_centr)) { df_pw <- data.frame( pathway = pw, cell_type = names(pw_centr$outdeg), outdeg_sender = pw_centr$outdeg, indeg_receiver = pw_centr$indeg, flowbet_mediator = if (!is.null(pw_centr$flowbet)) pw_centr$flowbet else NA, info_influencer = if (!is.null(pw_centr$info)) pw_centr$info else NA, stringsAsFactors = FALSE ) centr_list[[pw]] <- df_pw } } if (length(centr_list) > 0) { df_centr <- do.call(rbind, centr_list) rownames(df_centr) <- NULL write.csv(df_centr, file.path(output_dir, "signaling_roles.csv"), row.names = FALSE) cat(" Saved:", file.path(output_dir, "signaling_roles.csv"), "(", length(pathways), "pathways )\n") } }, error = function(e) { cat(" ⚠ Signaling roles export failed:", conditionMessage(e), "\n") }) # ------------------------------------------------------------------------- # 6. Top interactions summary (human-readable) # ------------------------------------------------------------------------- cat("6. Top interactions summary...\n") tryCatch({ # Top 20 by communication probability if (nrow(df_net) > 0) { df_top <- df_net[order(df_net$prob, decreasing = TRUE), ] df_top <- head(df_top, 20) write.csv(df_top, file.path(output_dir, "top_interactions.csv"), row.names = FALSE) cat(" Saved:", file.path(output_dir, "top_interactions.csv"), "\n") } }, error = function(e) { cat(" ⚠ Top interactions export failed:", conditionMessage(e), "\n") }) # ------------------------------------------------------------------------- # 7. CellChat object (CRITICAL for downstream use) # ------------------------------------------------------------------------- cat("7. CellChat analysis object (RDS)...\n") saveRDS(cellchat, file.path(output_dir, "cellchat_object.rds")) cat(" Saved:", file.path(output_dir, "cellchat_object.rds"), "\n") cat(" (Load with: cellchat <- readRDS('cellchat_object.rds'))\n") # ------------------------------------------------------------------------- # 8. Markdown report (always generated) # ------------------------------------------------------------------------- cat("8. Generating markdown report...\n") .generate_markdown_report(cellchat, df_net, df_pathway, output_dir) cat(" Saved:", file.path(output_dir, "analysis_report.md"), "\n") # ------------------------------------------------------------------------- # 9. PDF report (optional, via generate_report.R) # ------------------------------------------------------------------------- cat("9. Generating PDF report...\n") tryCatch({ source("scripts/generate_report.R") generate_report(cellchat, df_net, df_pathway, output_dir = output_dir) }, error = function(e) { cat(" PDF generation skipped:", conditionMessage(e), "\n") cat(" (Markdown report still available)\n") }) # ------------------------------------------------------------------------- # Summary # ------------------------------------------------------------------------- cat("\n=== Export Complete ===\n") cat("\nOutput files in '", output_dir, "/':\n") output_files <- list.files(output_dir, full.names = FALSE) for (f in sort(output_files)) { fsize <- file.size(file.path(output_dir, f)) cat(sprintf(" %-45s %s\n", f, .format_size(fsize))) } cat("\n") } # --- Helper: format file size ------------------------------------------------ .format_size <- function(bytes) { if (is.na(bytes)) return("") if (bytes < 1024) return(paste0(bytes, " B")) if (bytes < 1024^2) return(sprintf("%.1f KB", bytes / 1024)) return(sprintf("%.1f MB", bytes / 1024^2)) } # --- Helper: Markdown report ------------------------------------------------- .generate_markdown_report <- function(cellchat, df_net, df_pathway, output_dir) { pathways <- cellchat@netP$pathways cell_types <- levels(cellchat@idents) lines <- c( "# Cell-Cell Communication Analysis Report", "", paste("**Date:**", Sys.Date()), paste("**Tool:** CellChat v2"), "", "## Summary", "", paste("- **Cell types analyzed:**", length(cell_types)), paste("- **Significant interactions:**", nrow(df_net)), paste("- **Active signaling pathways:**", length(pathways)), "", "## Introduction", "", "This report presents cell-cell communication analysis results inferred from", "single-cell RNA-seq data using CellChat v2. CellChat identifies intercellular", "communication by modeling ligand-receptor interactions and quantifying", "communication probabilities between cell populations.", "", "### Cell Types", "", paste("-", cell_types), "", "## Results", "", "### Top Signaling Pathways", "" ) # Top pathways by information flow if (length(pathways) > 0) { pathway_prob <- cellchat@netP$prob if (!is.null(pathway_prob) && length(dim(pathway_prob)) == 3) { pathway_strength <- apply(pathway_prob, 3, sum) pathway_strength <- sort(pathway_strength, decreasing = TRUE) top_n <- min(15, length(pathway_strength)) lines <- c(lines, "| Rank | Pathway | Strength |", "|------|---------|----------|" ) for (i in seq_len(top_n)) { lines <- c(lines, sprintf("| %d | %s | %.4f |", i, names(pathway_strength)[i], pathway_strength[i])) } } } lines <- c(lines, "", "### Top Ligand-Receptor Interactions", "" ) if (nrow(df_net) > 0) { df_top <- df_net[order(df_net$prob, decreasing = TRUE), ] df_top <- head(df_top, 15) lines <- c(lines, "| Source | Target | Interaction | Pathway | Probability |", "|--------|--------|-------------|---------|-------------|" ) for (i in seq_len(nrow(df_top))) { lines <- c(lines, sprintf("| %s | %s | %s | %s | %.4f |", df_top$source[i], df_top$target[i], df_top$interaction_name_2[i], df_top$pathway_name[i], df_top$prob[i])) } } lines <- c(lines, "", "## Methods", "", "Cell-cell communication was inferred using CellChat v2 (Jin et al., Nature Communications 2021).", "Ligand-receptor interactions were identified from the CellChatDB database.", "Communication probabilities were computed using the triMean method.", "Network centrality analysis was performed to identify dominant senders, receivers, mediators, and influencers.", "", "## Conclusions", "", paste("- **", nrow(df_net), "** significant ligand-receptor interactions identified across **", length(pathways), "** signaling pathways"), paste("- Communication network analysis reveals dominant senders and receivers among", length(cell_types), "cell types"), "- Signaling role analysis identifies key mediator and influencer cell populations", "- These results can guide target identification for therapeutic intervention", "", "## References", "", "- Jin S, et al. Inference and analysis of cell-cell communication using CellChat. Nature Communications. 2021;12:1088.", "- Jin S, et al. CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics. Nature Protocols. 2024." ) writeLines(lines, file.path(output_dir, "analysis_report.md")) }