Clustering 3K PBMCs Analysis This repository contains the code for clustering analysis of 3K PBMCs (Peripheral Blood Mononuclear Cells) using single-cell RNA sequencing data.

Overview The project focuses on performing clustering analysis on the 3K PBMCs dataset. The provided Jupyter notebook (Clustering-3K-PBMCs.ipynb) contains the code for preprocessing the data, performing dimensionality reduction (PCA, t-SNE, UMAP), and clustering the cells using the Louvain algorithm. The analysis aims to identify distinct cell populations and visualize their distribution in reduced-dimensional space.

Dataset The analysis utilizes the Clustering 3K PBMCs dataset, which consists of single-cell RNA sequencing data from peripheral blood mononuclear cells. The dataset is processed using the provided code to perform quality control, filtering, and normalization.

Dependencies To run the code, make sure to install the following dependencies:

• scanpy • igraph • louvain

Usage • Install the required dependencies: pip install scanpy igraph louvain • Open the Jupyter notebook: Clustering-3K-PBMCs.ipynb • Follow the code in the notebook to analyze the 3K PBMCs dataset. • Modify the code or parameters as needed for your own analysis.