scRNA-seq Analysis Pipeline

A Scanpy-based single-cell RNA sequencing analysis pipeline for B cells across multiple timepoints, covering quality control, normalization, clustering, and visualization.

Dataset

• Cell type: B cells
• Timepoints: Day 2, Day 4, Day 6
• Format: 10x Genomics H5 (filtered feature-barcode matrix)
• Size: 36,306 cells × 36,601 genes

Sample	Cells
Day 2	15,285
Day 4	11,127
Day 6	9,894

Pipeline Steps

1. Data loading — Load 10x H5 files, standardize gene names, deduplicate, and merge samples with unique barcodes
2. Quality control — Calculate mitochondrial, ribosomal, and hemoglobin gene fractions; filter low-quality cells
3. Normalization — Normalize per cell, log1p transform
4. Feature selection — Identify highly variable genes
5. Dimensionality reduction — PCA, UMAP
6. Clustering — Leiden clustering
7. Visualization — UMAP plots colored by timepoint, cluster, and marker genes

Requirements

scanpy==1.12.1
anndata==0.12.16
pandas==2.3.3
numpy==2.4.6
scipy==1.17.1
matplotlib
seaborn
gtfparse
h5py

Usage

Open and run the notebook:

jupyter notebook scRNAseq_small_pipeline.ipynb