tags: #R #scRNA-seq #CITE-Seq #PCA #t-SNE #UMAP

Single-Cell RNA Sequencing and CITE-Seq Analysis

This repository contains resources and scripts for analyzing single-cell RNA sequencing (scRNA-seq) data, focusing on integrating RNA and surface protein expression data using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq). The primary goal is to provide reproducible workflows for scRNA-seq data analysis with practical insights into handling biological datasets.

Overview

The main analysis script, Bald-scRNAseq-HNSCC.R.md, describes the step-by-step approach for analyzing scRNA-seq data using R. It includes:

• Preprocessing and quality control of scRNA-seq data.
• Dimensionality reduction techniques like Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP).
• Clustering and identification of cell types.
• Integration of CITE-Seq protein markers.
• Visualization of results through high-quality plots.

Repository Structure

• Bald-scRNAseq-HNSCC.R.md: Markdown version of the R script, which provides a detailed narrative of the analysis pipeline, with code snippets and outputs.
• Bald-scRNAseq-HNSCC.R_images/: A directory containing images and plots generated during the analysis, referenced in the .md file.

How to Use

1. Clone the repository:

   git clone https://github.com/luenhchang/single_cell_RNA_sequencing_CITE_seq.git