Scanpy Bone Marrow

Single-cell RNA-seq analysis of the human bone marrow samples using the Scanpy framework, following the scverse tutorials.

Dataset

Two bone marrow samples (s1d1, s1d3) from the scverse example data, hosted on Figshare.

Pipeline Overview

1. Quality Control — Filter cells/genes, flag mitochondrial/ribosomal/hemoglobin content
2. Doublet Detection — Scrublet-based identification of multiplets
3. Normalization — Library size normalization and log-transformation
4. Feature Selection — Identify highly variable genes (batch-aware)
5. Dimensionality Reduction — PCA, nearest-neighbor graph, UMAP
6. Clustering — Leiden community detection
7. Annotation — Marker gene–based cell type assignment

Setup

Requirements

• Python 3.10+
• GitHub Codespaces (4-core / 16 GB RAM recommended) or local environment

Installation

python3 -m venv .venv
source .venv/bin/activate
pip install 'scanpy[leiden]' pooch scikit-image ipykernel
python -m ipykernel install --user --name=venv --display-name "Python (.venv)"

Running

Open main.ipynb in VS Code or Jupyter, select the .venv kernel, and run all cells.

Key Dependencies

Package	Purpose
scanpy	Core scRNA-seq analysis
anndata	Data structure for annotated matrices
pooch	Tutorial data retrieval
scikit-image	Required by Scrublet (doublet detection)
leidenalg	Leiden clustering algorithm

Notes

• Scrublet is memory-intensive. Use a 4-core / 16 GB machine if running on Codespaces.
• The .venv/ directory is excluded via .gitignore.

References

• Scanpy documentation
• scverse tutorials
• Luecken, M.D. et al. A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. NeurIPS Datasets and Benchmarks (2021).
• McCarthy, D.J. et al. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
• Wolock, S.L., Lopez, R. & Klein, A.M. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Systems 8, 281–291 (2019).
• Satija, R. et al. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology 33, 495–502 (2015).
• Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902 (2019).
• Zheng, G.X.Y. et al. Massively parallel digital transcriptional profiling of single cells. Nature Communications 8, 14049 (2017).
• McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv 1802.03426 (2018).
• Traag, V.A., Waltman, L. & van Eck, N.J. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9, 5233 (2019).
• Wolf, F.A., Angerer, P. & Theis, F.J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19, 15 (2018).