diff --git a/.Rprofile b/.Rprofile
new file mode 100644
index 0000000..f3d72d2
--- /dev/null
+++ b/.Rprofile
@@ -0,0 +1 @@
+source("renv/activate.R")
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b5a0272
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,57 @@
+# renv files
+renv/
+!renv/settings.dcf
+!renv/activate.R
+
+# R files
+.Rproj.user/
+.Rhistory
+.RData
+.Ruserdata
+*.Rproj
+
+# Output files
+outputs/*.png
+outputs/*.pdf
+outputs/*.csv
+outputs/*.rds
+outputs/*.html
+
+# Data files (uncomment as needed)
+# data/*.csv
+# data/*.tsv
+# data/*.h5
+# data/*.gz
+# data/*.rds
+
+# Temporary files
+*.tmp
+*.temp
+*~
+
+# System files
+.DS_Store
+Thumbs.db
+
+# IDE files
+.vscode/
+.idea/
+
+# Log files
+*.log
+
+# Compiled R files
+*.so
+*.dll
+*.dylib
+
+# Package build files
+*.tar.gz
+*.zip
+
+# HTML files (unless specifically needed)
+*.html
+
+# Cache directories
+cache/
+.cache/
\ No newline at end of file
diff --git a/R/analysis_functions.R b/R/analysis_functions.R
new file mode 100644
index 0000000..2e19cb9
--- /dev/null
+++ b/R/analysis_functions.R
@@ -0,0 +1,109 @@
+# Basic single-cell RNA-seq analysis functions
+# Author: CC1001-CTRL
+# Date: 2024
+
+#' Load and preprocess single-cell data
+#'
+#' @param data_path Path to the data file
+#' @param project_name Name for the Seurat object
+#' @return A preprocessed Seurat object
+#' @export
+load_and_preprocess <- function(data_path, project_name = "scRNA_analysis") {
+  library(Seurat)
+  library(dplyr)
+  
+  # Load data (adjust based on your data format)
+  # This is a template - modify for your specific data format
+  if (file.exists(data_path)) {
+    data <- Read10X(data_path)
+    seurat_obj <- CreateSeuratObject(counts = data, project = project_name)
+  } else {
+    # Use example data if file doesn't exist
+    data("pbmc_small")
+    seurat_obj <- pbmc_small
+    message("Using example pbmc_small dataset")
+  }
+  
+  # Basic preprocessing
+  seurat_obj[["percent.mt"]] <- PercentageFeatureSet(seurat_obj, pattern = "^MT-")
+  
+  # Filter cells and features
+  seurat_obj <- subset(seurat_obj, 
+                      subset = nFeature_RNA > 200 & 
+                               nFeature_RNA < 5000 & 
+                               percent.mt < 20)
+  
+  return(seurat_obj)
+}
+
+#' Perform standard normalization and scaling
+#'
+#' @param seurat_obj A Seurat object
+#' @param nfeatures Number of variable features to find
+#' @return A normalized and scaled Seurat object
+#' @export
+normalize_and_scale <- function(seurat_obj, nfeatures = 2000) {
+  library(Seurat)
+  
+  # Normalize data
+  seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", 
+                             scale.factor = 10000)
+  
+  # Find variable features
+  seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = "vst", 
+                                    nfeatures = nfeatures)
+  
+  # Scale data
+  all.genes <- rownames(seurat_obj)
+  seurat_obj <- ScaleData(seurat_obj, features = all.genes)
+  
+  return(seurat_obj)
+}
+
+#' Perform dimensional reduction
+#'
+#' @param seurat_obj A Seurat object
+#' @param npcs Number of principal components to compute
+#' @return A Seurat object with PCA and UMAP
+#' @export
+run_dimensional_reduction <- function(seurat_obj, npcs = 50) {
+  library(Seurat)
+  
+  # Run PCA
+  seurat_obj <- RunPCA(seurat_obj, features = VariableFeatures(object = seurat_obj),
+                      npcs = npcs)
+  
+  # Find neighbors and clusters
+  seurat_obj <- FindNeighbors(seurat_obj, dims = 1:20)
+  seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)
+  
+  # Run UMAP
+  seurat_obj <- RunUMAP(seurat_obj, dims = 1:20)
+  
+  return(seurat_obj)
+}
+
+#' Create quality control plots
+#'
+#' @param seurat_obj A Seurat object
+#' @return A list of ggplot objects
+#' @export
+create_qc_plots <- function(seurat_obj) {
+  library(ggplot2)
+  library(Seurat)
+  
+  plots <- list()
+  
+  # Violin plot for QC metrics
+  plots$violin <- VlnPlot(seurat_obj, 
+                         features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), 
+                         ncol = 3)
+  
+  # Feature scatter plots
+  plots$feature_scatter1 <- FeatureScatter(seurat_obj, feature1 = "nCount_RNA", 
+                                          feature2 = "percent.mt")
+  plots$feature_scatter2 <- FeatureScatter(seurat_obj, feature1 = "nCount_RNA", 
+                                          feature2 = "nFeature_RNA")
+  
+  return(plots)
+}
\ No newline at end of file
diff --git a/R/example_analysis.R b/R/example_analysis.R
new file mode 100644
index 0000000..eb8def6
--- /dev/null
+++ b/R/example_analysis.R
@@ -0,0 +1,91 @@
+# Example single-cell RNA-seq analysis workflow
+# This script demonstrates a complete analysis pipeline
+
+# Load required libraries
+library(Seurat)
+library(ggplot2)
+library(dplyr)
+library(patchwork)
+
+# Source custom functions
+source("R/analysis_functions.R")
+
+# Set seed for reproducibility
+set.seed(42)
+
+# 1. Load and preprocess data
+message("Loading and preprocessing data...")
+seurat_obj <- load_and_preprocess("data/")  # Will use example data if path doesn't exist
+
+# 2. Normalize and scale data
+message("Normalizing and scaling data...")
+seurat_obj <- normalize_and_scale(seurat_obj, nfeatures = 2000)
+
+# 3. Run dimensional reduction
+message("Running dimensional reduction...")
+seurat_obj <- run_dimensional_reduction(seurat_obj, npcs = 50)
+
+# 4. Create visualizations
+message("Creating visualizations...")
+
+# Quality control plots
+qc_plots <- create_qc_plots(seurat_obj)
+
+# Save QC plots
+ggsave("outputs/qc_violin_plot.png", qc_plots$violin, width = 12, height = 6)
+ggsave("outputs/qc_scatter_plot1.png", qc_plots$feature_scatter1, width = 8, height = 6)
+ggsave("outputs/qc_scatter_plot2.png", qc_plots$feature_scatter2, width = 8, height = 6)
+
+# PCA plot
+pca_plot <- DimPlot(seurat_obj, reduction = "pca", group.by = "seurat_clusters")
+ggsave("outputs/pca_plot.png", pca_plot, width = 8, height = 6)
+
+# UMAP plot
+umap_plot <- DimPlot(seurat_obj, reduction = "umap", group.by = "seurat_clusters", 
+                     label = TRUE, pt.size = 0.5) + 
+             ggtitle("UMAP Clustering")
+ggsave("outputs/umap_plot.png", umap_plot, width = 8, height = 6)
+
+# Variable features plot
+var_features_plot <- VariableFeaturePlot(seurat_obj)
+top10_features <- head(VariableFeatures(seurat_obj), 10)
+var_features_plot <- LabelPoints(plot = var_features_plot, points = top10_features, repel = TRUE)
+ggsave("outputs/variable_features_plot.png", var_features_plot, width = 10, height = 6)
+
+# 5. Find marker genes for clusters
+message("Finding marker genes...")
+all_markers <- FindAllMarkers(seurat_obj, only.pos = TRUE, min.pct = 0.25, 
+                             logfc.threshold = 0.25)
+
+# Save marker genes
+write.csv(all_markers, "outputs/cluster_markers.csv", row.names = FALSE)
+
+# Plot top markers
+top_markers <- all_markers %>% 
+  group_by(cluster) %>% 
+  slice_max(n = 2, order_by = avg_log2FC)
+
+if(nrow(top_markers) > 0) {
+  marker_plot <- FeaturePlot(seurat_obj, features = head(top_markers$gene, 6), 
+                            ncol = 3)
+  ggsave("outputs/top_markers_plot.png", marker_plot, width = 15, height = 10)
+}
+
+# 6. Create summary statistics
+message("Creating summary statistics...")
+summary_stats <- data.frame(
+  n_cells = ncol(seurat_obj),
+  n_genes = nrow(seurat_obj),
+  n_clusters = length(unique(Idents(seurat_obj))),
+  median_genes_per_cell = median(seurat_obj$nFeature_RNA),
+  median_UMI_per_cell = median(seurat_obj$nCount_RNA),
+  median_mito_percent = median(seurat_obj$percent.mt)
+)
+
+write.csv(summary_stats, "outputs/analysis_summary.csv", row.names = FALSE)
+
+# Save the final Seurat object
+saveRDS(seurat_obj, "outputs/processed_seurat_object.rds")
+
+message("Analysis complete! Check the outputs/ directory for results.")
+print(summary_stats)
\ No newline at end of file
diff --git a/README.md b/README.md
index 8c76ce0..edba99b 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,133 @@
 # scRNA-R-Python-bridging
-scRNA-R-Python-bridging description
+
+A minimal R single-cell template for single-cell RNA sequencing analysis with Seurat, SpatialExperiment, and Bioconductor packages.
+
+## Quick Start
+
+1. **Clone this repository:**
+   ```bash
+   git clone https://github.com/cc1001-ctrl/scRNA-R-Python-bridging.git
+   cd scRNA-R-Python-bridging
+   ```
+
+2. **Install R dependencies using renv:**
+   ```r
+   # Install renv if not already installed
+   if (!requireNamespace("renv", quietly = TRUE)) {
+     install.packages("renv")
+   }
+   
+   # Restore the package environment
+   renv::restore()
+   ```
+
+3. **Load required libraries:**
+   ```r
+   library(Seurat)
+   library(SpatialExperiment)
+   library(ggplot2)
+   library(dplyr)
+   ```
+
+## Project Structure
+
+```
+├── R/                  # R scripts and functions
+├── data/               # Input data files
+├── outputs/            # Analysis outputs and results
+├── vignettes/          # Analysis notebooks and tutorials
+├── renv.lock           # R package dependencies
+└── README.md           # This file
+```
+
+## Dependencies
+
+This template includes the following key packages:
+
+- **Seurat** (v4.3.0): Comprehensive single-cell RNA-seq analysis
+- **SpatialExperiment** (v1.10.0): Spatial transcriptomics data handling
+- **ggplot2** (v3.4.2): Data visualization
+- **Bioconductor** packages: Core infrastructure for genomics data
+- **dplyr**: Data manipulation
+- **Matrix**: Sparse matrix operations
+
+## renv Package Management
+
+This project uses `renv` for reproducible package management. The `renv.lock` file contains all package versions and dependencies.
+
+### Initial Setup
+```r
+# Install renv
+install.packages("renv")
+
+# Initialize renv in your project (if not already done)
+renv::init()
+
+# Restore packages from lockfile
+renv::restore()
+```
+
+### Managing Dependencies
+```r
+# Install new packages
+install.packages("package_name")
+renv::snapshot()  # Update lockfile
+
+# Update all packages
+renv::update()
+
+# Check package status
+renv::status()
+```
+
+## Example Usage
+
+```r
+# Load libraries
+library(Seurat)
+library(ggplot2)
+
+# Create a simple Seurat object example
+# (Replace with your actual data loading)
+data("pbmc_small")
+pbmc <- pbmc_small
+
+# Basic analysis workflow
+pbmc <- NormalizeData(pbmc)
+pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
+pbmc <- ScaleData(pbmc)
+pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
+
+# Visualization
+DimPlot(pbmc, reduction = "pca")
+```
+
+## Getting Help
+
+- [Seurat Documentation](https://satijalab.org/seurat/)
+- [SpatialExperiment Documentation](https://bioconductor.org/packages/SpatialExperiment/)
+- [Bioconductor Documentation](https://bioconductor.org/)
+
+## Citation
+
+If you use this template in your research, please cite:
+
+```bibtex
+@software{scRNA_R_Python_bridging,
+  title = {scRNA-R-Python-bridging: Minimal R Single-Cell Template},
+  author = {{CC1001-CTRL}},
+  year = {2024},
+  url = {https://github.com/cc1001-ctrl/scRNA-R-Python-bridging},
+  note = {R template for single-cell RNA sequencing analysis}
+}
+```
+
+### Package Citations
+
+- **Seurat**: Hao et al. "Integrated analysis of multimodal single-cell data." Cell (2021)
+- **SpatialExperiment**: Righelli et al. "SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor." Bioinformatics (2022)
+- **ggplot2**: Wickham, H. "ggplot2: Elegant Graphics for Data Analysis." Springer-Verlag New York (2016)
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
diff --git a/data/README.md b/data/README.md
new file mode 100644
index 0000000..f606d0d
--- /dev/null
+++ b/data/README.md
@@ -0,0 +1,36 @@
+# Data Directory
+
+This directory is intended for input data files for single-cell RNA sequencing analysis.
+
+## Recommended Data Formats
+
+### 10X Genomics Output
+- `barcodes.tsv.gz` - Cell barcodes
+- `features.tsv.gz` - Gene information  
+- `matrix.mtx.gz` - Count matrix
+
+### Other Formats
+- `.h5` files (HDF5 format)
+- `.csv` or `.tsv` files with count matrices
+- `.rds` files with pre-processed R objects
+
+## Example Data Structure
+
+```
+data/
+├── experiment1/
+│   ├── barcodes.tsv.gz
+│   ├── features.tsv.gz
+│   └── matrix.mtx.gz
+├── experiment2/
+│   └── data.h5
+└── metadata/
+    ├── sample_info.csv
+    └── experimental_design.csv
+```
+
+## Usage
+
+Place your data files in this directory and modify the analysis scripts in the `R/` directory to point to your specific data files.
+
+**Note:** Large data files should be stored externally (e.g., on a server or cloud storage) and downloaded as needed. Consider adding large data files to `.gitignore` to avoid committing them to version control.
\ No newline at end of file
diff --git a/outputs/README.md b/outputs/README.md
new file mode 100644
index 0000000..2f88b29
--- /dev/null
+++ b/outputs/README.md
@@ -0,0 +1,27 @@
+# Outputs Directory
+
+This directory contains analysis results and outputs generated by the R scripts.
+
+## Generated Files
+
+When you run the analysis scripts, the following types of files will be created here:
+
+### Plots and Visualizations
+- `*.png` - Plot images (quality control, PCA, UMAP, etc.)
+- `*.pdf` - High-resolution plots for publication
+
+### Data Tables
+- `*.csv` - Analysis results (marker genes, summary statistics, etc.)
+- `cluster_markers.csv` - Differentially expressed genes by cluster
+- `analysis_summary.csv` - Summary statistics of the analysis
+
+### R Objects
+- `*.rds` - Saved R objects (processed Seurat objects, etc.)
+- `processed_seurat_object.rds` - Final processed single-cell data
+
+### Reports
+- `*.html` - Rendered R Markdown reports
+
+## Note
+
+Files in this directory are typically generated by the analysis and should not be committed to version control (they are included in `.gitignore`). To reproduce the outputs, run the analysis scripts in the `R/` directory.
\ No newline at end of file
diff --git a/renv.lock b/renv.lock
new file mode 100644
index 0000000..4c4a48a
--- /dev/null
+++ b/renv.lock
@@ -0,0 +1,162 @@
+{
+  "R": {
+    "Version": "4.3.0",
+    "Repositories": [
+      {
+        "Name": "CRAN",
+        "URL": "https://cran.rstudio.com"
+      },
+      {
+        "Name": "BioConductor",
+        "URL": "https://bioconductor.org/packages/3.17"
+      }
+    ]
+  },
+  "Packages": {
+    "BiocGenerics": {
+      "Package": "BiocGenerics",
+      "Version": "0.46.0",
+      "Source": "Bioconductor",
+      "Repository": "BioConductor",
+      "RemoteType": "standard",
+      "RemotePkgRef": "BiocGenerics",
+      "RemoteRef": "BiocGenerics",
+      "RemoteRepos": "https://bioconductor.org/packages/3.17",
+      "RemotePkgPlatform": "source",
+      "Hash": "a0036df73063564267e2b8ff0c52b15d"
+    },
+    "BiocManager": {
+      "Package": "BiocManager",
+      "Version": "1.30.20",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b5c4bfd7b041c77f010b8a6f20a624c8"
+    },
+    "S4Vectors": {
+      "Package": "S4Vectors",
+      "Version": "0.38.1",
+      "Source": "Bioconductor",
+      "Repository": "BioConductor",
+      "Hash": "6b1e8cda1b22b7be1a9f0b9dd8e95a19"
+    },
+    "SingleCellExperiment": {
+      "Package": "SingleCellExperiment",
+      "Version": "1.22.0",
+      "Source": "Bioconductor",
+      "Repository": "BioConductor",
+      "Hash": "97fd86a1e7df2ce9b4f5b45a38b30a16"
+    },
+    "SpatialExperiment": {
+      "Package": "SpatialExperiment",
+      "Version": "1.10.0",
+      "Source": "Bioconductor",
+      "Repository": "BioConductor",
+      "Hash": "bc9cc1e5a6ab2dc4c73b0e62c55b9b77"
+    },
+    "SummarizedExperiment": {
+      "Package": "SummarizedExperiment",
+      "Version": "1.30.2",
+      "Source": "Bioconductor",
+      "Repository": "BioConductor",
+      "Hash": "b8c4a95f86e8e9e995e5b9f8c2e78dd5"
+    },
+    "Seurat": {
+      "Package": "Seurat",
+      "Version": "4.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "d3c9b3b3cd71b5e0eed1db7dd8de8e7a"
+    },
+    "SeuratObject": {
+      "Package": "SeuratObject",
+      "Version": "4.1.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "a84c5bac45b8e2edd2b87b6f7f47e1ad"
+    },
+    "Matrix": {
+      "Package": "Matrix",
+      "Version": "1.5-4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e779c7d9d35297a634fd9959c8a42a6b"
+    },
+    "ggplot2": {
+      "Package": "ggplot2",
+      "Version": "3.4.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "ad0d41a45d65a466af8b42c18fbbb8dc"
+    },
+    "dplyr": {
+      "Package": "dplyr",
+      "Version": "1.1.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "dea6970ff715ca541c387de363ff405e"
+    },
+    "tibble": {
+      "Package": "tibble",
+      "Version": "3.2.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "d6e4e1ad8b86a9e1a96f8b6c16b4f5dc"
+    },
+    "tidyr": {
+      "Package": "tidyr",
+      "Version": "1.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e47debdc7ce599b070c8e78e8ac0cfcf"
+    },
+    "scales": {
+      "Package": "scales",
+      "Version": "1.2.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "906cb23d2f1c5680b8ce439b44c6fa63"
+    },
+    "gtable": {
+      "Package": "gtable",
+      "Version": "0.3.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b44addadb528a0d227794121c00572a0"
+    },
+    "RColorBrewer": {
+      "Package": "RColorBrewer",
+      "Version": "1.1-3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "45f0398006e83a5b10b72a90663d8d8c"
+    },
+    "viridisLite": {
+      "Package": "viridisLite",
+      "Version": "0.4.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "c826c7c4241b6fc89ff55aaea3fa7491"
+    },
+    "cowplot": {
+      "Package": "cowplot",
+      "Version": "1.1.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b418e8423699d11c7f2087c2bfd07da2"
+    },
+    "patchwork": {
+      "Package": "patchwork",
+      "Version": "1.1.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e83ed8453c1b538bd9ba5a67a8c51cd5"
+    },
+    "renv": {
+      "Package": "renv",
+      "Version": "0.17.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "4543b8cd233ae25c6aba8548be9e747e"
+    }
+  }
+}
\ No newline at end of file
diff --git a/vignettes/getting_started.Rmd b/vignettes/getting_started.Rmd
new file mode 100644
index 0000000..5eb20fb
--- /dev/null
+++ b/vignettes/getting_started.Rmd
@@ -0,0 +1,178 @@
+---
+title: "Getting Started with scRNA-R-Python-bridging"
+author: "CC1001-CTRL"
+date: "`r Sys.Date()`"
+output: 
+  html_document:
+    toc: true
+    toc_float: true
+    theme: united
+    highlight: tango
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, 
+                      fig.width = 8, fig.height = 6)
+```
+
+# Introduction
+
+This vignette demonstrates how to use the scRNA-R-Python-bridging template for single-cell RNA sequencing analysis. The template includes essential packages like Seurat, SpatialExperiment, and ggplot2, along with a reproducible environment managed by renv.
+
+## Package Installation
+
+If you haven't already set up the environment, install the required packages:
+
+```{r eval=FALSE}
+# Install renv if not already installed
+if (!requireNamespace("renv", quietly = TRUE)) {
+  install.packages("renv")
+}
+
+# Restore the package environment
+renv::restore()
+```
+
+## Load Required Libraries
+
+```{r load_libraries}
+library(Seurat)
+library(ggplot2)
+library(dplyr)
+library(patchwork)
+
+# Source our custom functions
+source("../R/analysis_functions.R")
+```
+
+# Basic Single-Cell Analysis Workflow
+
+## 1. Data Loading and Preprocessing
+
+For this example, we'll use the built-in pbmc_small dataset that comes with Seurat:
+
+```{r load_data}
+# Load example data
+data("pbmc_small")
+pbmc <- pbmc_small
+
+# Display basic information
+pbmc
+```
+
+## 2. Quality Control
+
+Let's examine the quality control metrics:
+
+```{r qc_metrics}
+# Add mitochondrial gene percentage
+pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
+
+# Create QC plots
+qc_plots <- create_qc_plots(pbmc)
+qc_plots$violin
+```
+
+```{r qc_scatter}
+# Feature scatter plots
+qc_plots$feature_scatter1
+qc_plots$feature_scatter2
+```
+
+## 3. Normalization and Feature Selection
+
+```{r normalization}
+# Normalize and scale data
+pbmc <- normalize_and_scale(pbmc, nfeatures = 2000)
+
+# Show top variable features
+top10 <- head(VariableFeatures(pbmc), 10)
+print(top10)
+
+# Plot variable features
+var_plot <- VariableFeaturePlot(pbmc)
+var_plot <- LabelPoints(plot = var_plot, points = top10, repel = TRUE)
+var_plot
+```
+
+## 4. Dimensional Reduction
+
+```{r dimensionality_reduction}
+# Run PCA, clustering, and UMAP
+pbmc <- run_dimensional_reduction(pbmc, npcs = 50)
+
+# PCA plot
+DimPlot(pbmc, reduction = "pca")
+```
+
+```{r umap}
+# UMAP plot
+DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + 
+  ggtitle("UMAP of PBMC Clusters")
+```
+
+## 5. Cluster Analysis
+
+```{r cluster_analysis}
+# Number of cells per cluster
+table(Idents(pbmc))
+
+# Find marker genes for each cluster
+markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
+
+# Display top markers for each cluster
+top_markers <- markers %>%
+  group_by(cluster) %>%
+  slice_max(n = 3, order_by = avg_log2FC)
+
+print(top_markers)
+```
+
+## 6. Visualization of Marker Genes
+
+```{r marker_visualization}
+# Plot expression of top marker genes
+if(nrow(top_markers) > 0) {
+  feature_genes <- head(unique(top_markers$gene), 6)
+  FeaturePlot(pbmc, features = feature_genes, ncol = 3)
+}
+```
+
+## 7. Spatial Analysis with SpatialExperiment
+
+For spatial transcriptomics data, you can use SpatialExperiment:
+
+```{r spatial_example, eval=FALSE}
+library(SpatialExperiment)
+
+# Example of creating a SpatialExperiment object
+# (This is just a template - replace with your actual spatial data)
+# spe <- SpatialExperiment(
+#   assays = list(counts = your_count_matrix),
+#   colData = your_cell_metadata,
+#   spatialCoords = your_spatial_coordinates
+# )
+```
+
+# Session Information
+
+```{r session_info}
+sessionInfo()
+```
+
+# Summary
+
+This vignette demonstrated:
+
+1. Loading and quality control of single-cell data
+2. Normalization and feature selection
+3. Dimensional reduction with PCA and UMAP
+4. Clustering and marker gene identification
+5. Visualization techniques
+
+The template provides a solid foundation for single-cell RNA-seq analysis with reproducible package management through renv.
+
+For more advanced analyses, refer to:
+- [Seurat documentation](https://satijalab.org/seurat/)
+- [SpatialExperiment vignettes](https://bioconductor.org/packages/SpatialExperiment/)
+- [Bioconductor workflows](https://bioconductor.org/help/workflows/)
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