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Simple-R-coding-for-single-cell-RNA-sequencing

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📘 Single-cell RNA-seq Data Integration and Metadata Setup

Author: [Deeksha Sharma]

Purpose: Load, merge, and annotate 10X Genomics scRNA-seq data

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1. Load Required Libraries

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Core data manipulation & visualization

library(dplyr) library(tidyverse) library(ggplot2) library(ggrepel) library(patchwork) library(ggpubr)

Seurat ecosystem for scRNA-seq analysis

library(Seurat) library(SeuratObject) library(glmGamPoi)

Functional analysis

library(clusterProfiler) library(org.Mm.eg.db) library(DOSE) library(enrichR)

Misc utilities

library(tibble) library(stringr) library(openxlsx) library(writexl) library(parallel) library(future) library(CellChat) library(monocle3) library(Nebulosa) library(pheatmap)

Optional: For debugging or development

library(devtools)

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2. Set Up Directory Structure

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Define and create output directories for results

tables_dir <- file.path(getwd(), "results", "tables") figures_dir <- file.path(getwd(), "results", "figures")

dir.create(tables_dir, recursive = TRUE, showWarnings = FALSE) dir.create(figures_dir, recursive = TRUE, showWarnings = FALSE)

##Check directory cat("Working directory:", getwd(), "\n") cat("Results directories initialized.\n")

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3. Load and Create Seurat Objects

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🔹 Define base data paths for clarity

base_path <- "/home/deekshas/R/7.R DATA and Scripts/Burness Data"

PYMT samples (Vehicle or control)

pymt_paths <- file.path(base_path, "PYMT DATA", paste0("11529-CH-", 1:6, "_sample_filtered_feature_bc_matrix"))

D2A1 samples (Treatment and control)

d2a1_paths <- file.path(base_path, "D2A1_CT1", paste0("11529-CH-", 7:12, "_sample_filtered_feature_bc_matrix"))

Function to read and create Seurat objects

create_obj <- function(path) { CreateSeuratObject(counts = Read10X(data.dir = path)) }

Read all 12 datasets

all_paths <- c(pymt_paths, d2a1_paths) seurat_list <- lapply(all_paths, create_obj)

Assign sample names for tracking

sample_names <- paste0("obj", seq_along(seurat_list)) names(seurat_list) <- sample_names

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4. Merge Seurat Objects

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merged_samples <- merge( x = seurat_list[[1]], y = seurat_list[-1], add.cell.ids = sample_names )

cat("✅ Merged", length(seurat_list), "samples into one Seurat object.\n") print(merged_samples)

Save merged object

saveRDS(merged_samples, file = "all_1-merged-object.RDS")

(Optional) Reload to confirm

merged_samples <- readRDS("all_1-merged-object.RDS")

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5. Join Layers and Cleanup

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obj <- JoinLayers(merged_samples)

Remove intermediate objects to free memory

rm(list = sample_names) gc() # Run garbage collection

Save joint object

saveRDS(obj, file = "all_1-joint-object.RDS")

Reload to confirm

obj <- readRDS("all_1-joint-object.RDS")

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6. Annotate Metadata (Sample, Cell Line, Treatment)

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Extract barcodes and sample IDs from cell names

rownames(obj[[]]) # View structure sample.by.barcode <- word(rownames(obj[[]]), start = 1, end = 1, sep = fixed("_"))

Add sample information to metadata

obj$sample <- sample.by.barcode

Define cell line assignments

cell.line <- rep(NA, length(sample.by.barcode)) cell.line[sample.by.barcode %in% paste0("11529-CH-", 1:6)] <- "PYMT" cell.line[sample.by.barcode %in% paste0("11529-CH-", 7:12)] <- "D2A1"

obj$cell_line <- cell.line

Define treatment conditions

treatment <- rep(NA, length(sample.by.barcode)) treatment[sample.by.barcode %in% c("11529-CH-1", "11529-CH-2", "11529-CH-3", "11529-CH-7", "11529-CH-8", "11529-CH-9")] <- "Vehicle" treatment[sample.by.barcode %in% c("11529-CH-4", "11529-CH-5", "11529-CH-6", "11529-CH-10", "11529-CH-11", "11529-CH-12")] <- "ZBC260"

obj$treatment <- treatment

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7. Save Final Annotated Object

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saveRDS(obj, file = "D2A1plus_PYMT_1-merged-object.RDS")

cat("✅ Final Seurat object saved with metadata annotations.\n")

Inspect metadata

print(obj) head(obj@meta.data)