SCRNA_seq_workflow This repository contains the workflow and analysis scripts for single-cell RNA sequencing (scRNA-seq) data. The project aims to uncover cellular heterogeneity and understand the transcriptomic landscape at the single-cell level. The analysis is performed using the Seurat package in R, focusing on data preprocessing, normalization, dimensionality reduction, clustering, and differential expression analysis.

Getting Started Prerequisites R (version 4.3.1 or higher) Bioconductor (version 3.17 or higher) Required R packages: Seurat GEOquery Biobase

Installation Ensure that R and Bioconductor are installed. Then, install the necessary R packages using the following commands: install.packages("Seurat") if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("GEOquery")

Downloading the Dataset The dataset used in this analysis is available from the GEO database under the accession number GSE138709. The GEOquery package is used to download the dataset directly into R.

Analysis Overview Data Preprocessing: Initial processing of the raw scRNA-seq data to filter out low-quality cells and genes. Normalization: Normalization of the data to remove technical variations while preserving biological differences. Dimensionality Reduction: Use of PCA, t-SNE, and UMAP to reduce the dataset to a lower-dimensional space for visualization and clustering. Clustering: Identification of cell clusters based on their gene expression profiles. Differential Expression Analysis: Determination of marker genes that define each cluster. Annotation and Interpretation: Annotation of clusters based on marker genes and integration of biological knowledge to interpret the results.

Usage The analysis is encapsulated in R scripts/notebooks. To replicate the analysis or apply it to your data, follow the steps outlined in the scripts, adjusting parameters and paths as necessary.

Contributing Contributions to improve the analysis or extend its capabilities are welcome. Please feel free to fork the repository and submit pull requests.

Authors Saheed Adeyanju

Acknowledgments The authors of the Seurat package for providing comprehensive tools for scRNA-seq data analysis. The GEO database for hosting the scRNA-seq dataset used in this analysis.