This repository contains code and data for analyzing single-cell RNA sequencing (scRNA-seq) data in breast cancer. The goal of this project is to explore and identify cellular heterogeneity, gene expression patterns, and potential biomarkers within breast cancer samples at a single-cell resolution.
Overview: Single-cell RNA sequencing provides valuable insights into the diverse cell populations in breast cancer tissues. By analyzing this data, we can identify cell subtypes, study gene expression variability, and gain insights into tumor microenvironments that are crucial for developing targeted cancer therapies. This repository includes pre-processing, clustering, visualization, and analysis steps for breast cancer scRNA-seq data.
- data: Contains sample data and metadata used in this analysis (or instructions to access large datasets).
- src: Contains scripts for data processing, clustering, differential expression analysis, and visualization.
- notebooks: Jupyter notebooks detailing each step of the analysis pipeline.
- results: Output results such as plots, tables, and processed datasets.
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Data Preprocessing: Scripts for quality control, normalization, and scaling of raw scRNA-seq data.
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Clustering and Visualization: Code for clustering cells, generating UMAP or t-SNE plots, and visualizing gene expression.
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Differential Expression Analysis: Identifies marker genes and differential expression between clusters.
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Biomarker Discovery: Tools to identify potential biomarkers specific to cell populations in breast cancer.