Single-Cell Transcriptomic Atlas of Cardiac Development

April 9, 2026 · View on GitHub

An end-to-end scRNA-seq pipeline mapping cell type heterogeneity in developing heart tissue; from raw counts to annotated cell atlases, with a cloud-native Nextflow + AWS Batch architecture.

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TL;DR

Built an end-to-end scRNA-seq pipeline using Scanpy + Nextflow + AWS Batch
Analysed 11,000+ cells across 18,000+ genes
Identified 10 cardiac cell types via unsupervised clustering
Designed for scalable cloud execution (AWS Batch + S3)
Produced publication-ready visualisations (UMAP, marker gene plots)

Demonstrates: single-cell analysis, pipeline engineering, cloud scalability

This project presents a complete single-cell RNA sequencing (scRNA-seq) analysis pipeline applied to developing heart tissue, using the publicly available Mouse (Mus musculus) Heart Cell Atlas dataset (E-MTAB-6173). Starting from raw count matrices, the pipeline performs quality control, normalisation, dimensionality reduction, unsupervised clustering, marker gene identification, and cell type annotation, producing a fully annotated cardiac cell atlas with publication-quality visualisations.

The analysis successfully identifies 10 distinct cardiac cell populations across 11,046 cells, revealing the transcriptional heterogeneity of the developing heart. The pipeline is additionally designed for cloud-native execution using Nextflow for workflow orchestration and AWS Batch for scalable compute, with the full infrastructure (S3 bucket, compute environment, job queue) configured and ready for deployment.

Biological Problem & How We Solved It

The Problem

The developing heart is composed of a highly diverse array of cell types: cardiomyocytes, fibroblasts, endothelial cells, progenitors and more, each governed by distinct transcriptional programmes. Traditional bulk RNA-seq cannot resolve this heterogeneity because it averages gene expression signals across all cells, masking rare populations and cell-type-specific signals entirely.

This creates a fundamental gap in our understanding of:

How cardiac cell types emerge and diversify during development
Which genes define each cardiac cell population
What rare progenitor states exist in the developing heart
How transcriptional heterogeneity relates to cardiac disease

How We Solved It

We applied single-cell RNA sequencing (scRNA-seq) analysis using the Scanpy framework, which profiles each of the 11,046 cells individually across 18,727 genes. Our approach:

Quality control - Removed low-quality cells, empty droplets and doublets to ensure only healthy cells entered the analysis
Normalisation - Corrected for technical variation in sequencing depth across cells
Dimensionality reduction - Used PCA and UMAP to compress 18,727 genes into interpretable 2D visualisations
Unsupervised clustering - Applied the Leiden algorithm to group transcriptionally similar cells without prior knowledge
Marker gene analysis - Identified signature genes for each cluster using the Wilcoxon rank-sum test
Cell type annotation - Matched marker genes to known cardiac cell type signatures from the literature

The result is a fully resolved cardiac cell atlas that makes the invisible visible, revealing 10 distinct populations that bulk RNA-seq would have merged into noise.

Results

Metric	Value
Total cells analysed	11,046
Genes measured	18,727
Highly variable genes	2,504
Cell clusters identified	13
Distinct cell types annotated	10

Cell Types Identified

Cell Type	Biological Role	Key Markers
Cardiomyocytes	Heart muscle contraction	Tnnt2
Fibroblasts	Structural support & ECM	Egr1, Gpx3
Endothelial Cells	Blood vessel lining	Pecam1, Cdh5
Smooth Muscle Cells	Vascular tone regulation	Acta2, Tagln
Pericytes	Microvascular stabilisation	Rgs5, Cspg4
Macrophages	Cardiac immune surveillance	C1qa, Cd68
Neural Cells	Cardiac innervation	Tubb3, Ncam1
Epicardial Cells	Outer heart lining	Wt1
Immune Cells	Inflammatory response	Cd68
Progenitor Cells	Undifferentiated cardiac stem cells	Wt1, Ncam1

Tools & Technologies

Category	Tools
Language	Python 3.14
scRNA-seq Analysis	Scanpy, AnnData
Pipeline Orchestration	Nextflow 25.10.2
Cloud Storage	AWS S3
Cloud Compute	AWS Batch (infrastructure configured)
Containerisation	Docker
Gene ID Conversion	MyGene
Data Processing	Pandas, NumPy, SciPy
Visualisation	Matplotlib, Scanpy plots
IDE	VS Code + Jupyter Notebooks

Dataset

Field	Details
Name	Single cell RNA-Seq of the murine non-myocyte cardiac cellulome
Accession	E-MTAB-6173
Source	EMBL-EBI Single Cell Expression Atlas
URL	https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6173
Format	Matrix Market (.mtx) - Raw Counts
Cells	11,057
Genes	18,742
Species	Mouse (Mus musculus)
Tissue	Developing heart
Accessed	April 2026

How to Run

Prerequisites

pip install scanpy anndata pandas numpy scipy matplotlib mygene

1. Clone the repository

git clone https://github.com/Naila-Srivastava/scRNA-Seq-Cardiac-Development.git
cd scRNA-Seq-Cardiac-Development

2. Download the dataset

Go to https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6173/downloads

Download the Raw Counts Matrix Market files and place them in data/:

E-MTAB-6173.aggregated_filtered_counts.mtx
E-MTAB-6173.aggregated_filtered_counts.mtx_cols
E-MTAB-6173.aggregated_filtered_counts.mtx_rows

3. Run the notebook

Open notebooks/scrna_cardiac.ipynb in VS Code and run all cells sequentially.

4. Cloud execution (infrastructure ready)

The Nextflow + AWS Batch pipeline is fully configured. To deploy on AWS:

# Configure AWS credentials
aws configure

# Upload data to S3
aws s3 cp data/ s3://your-bucket/data/ --recursive

# Run pipeline on AWS Batch
cd pipeline
nextflow run main.nf \
    -profile aws \
    -bucket-dir s3://your-bucket/nextflow-work/ \
    --input_dir s3://your-bucket/data/ \
    --output_dir s3://your-bucket/results/

Note: Full cloud execution requires VPC and IAM role configuration specific to your AWS environment.

Methodology

Raw Count Matrix (11,057 cells × 18,742 genes)
        │
        ▼
1. DATA LOADING
   Load .mtx, barcodes and gene files
   Construct AnnData object
        │
        ▼
2. QUALITY CONTROL
   Filter cells: min 200 genes, max 5,000 genes
   Filter genes: detected in at least 3 cells
   Result: 11,046 cells × 18,727 genes
        │
        ▼
3. NORMALISATION & LOG-TRANSFORMATION
   Library size normalisation (target = 10,000 counts)
   Log1p transformation
   Raw counts preserved in adata.raw
        │
        ▼
4. HIGHLY VARIABLE GENE SELECTION
   2,504 genes selected
   (min_mean=0.0125, max_mean=3, min_disp=0.5)
        │
        ▼
5. SCALING & PCA
   Scale to unit variance (max_value=10)
   50 principal components computed
   Elbow identified at PC15
        │
        ▼
6. NEIGHBOURHOOD GRAPH & UMAP
   10 neighbours, 15 PCs
   UMAP for 2D visualisation
        │
        ▼
7. LEIDEN CLUSTERING
   Resolution = 0.5
   13 clusters identified
        │
        ▼
8. GENE SYMBOL CONVERSION
   Ensembl IDs → Gene symbols via MyGene
   Mouse genome (Mus musculus)
        │
        ▼
9. MARKER GENE ANALYSIS
   Wilcoxon rank-sum test
   Top marker genes per cluster identified
        │
        ▼
10. CELL TYPE ANNOTATION
    10 cardiac cell types annotated
    Based on known marker gene signatures
        │
        ▼
VISUALISATIONS
(UMAP, Dotplot, Violin plots)

Features

End-to-end pipeline - From raw count matrix to annotated cell atlas in a single notebook
Gene symbol conversion - Automated Ensembl ID to gene symbol mapping using MyGene
Unsupervised clustering - Leiden algorithm identifies cell populations without prior knowledge
Publication-quality visualisations - UMAP, dotplot and violin plots saved automatically
Cloud-ready architecture - Nextflow pipeline with AWS Batch and S3 integration fully configured
Reproducible - All parameters documented and results saved as .h5ad for downstream analysis
Scalable - Pipeline designed to handle millions of cells on cloud infrastructure

Data quality was excellent: Only 11 out of 11,057 cells were removed during QC, indicating a high-quality, well-processed dataset.
PCA elbow at PC15: The variance ratio plot clearly showed diminishing returns after PC15, a common and expected finding in well-normalised scRNA-seq data.
13 clusters map to 10 biologically meaningful cell types: The Leiden algorithm successfully resolved all major cardiac cell populations expected during heart development without any prior biological knowledge.
Wt1 confirms progenitor identity: The strong and specific expression of Wt1 in the Progenitor Cells cluster is a textbook cardiac progenitor marker, independently validating our annotations.
Pericytes show clean Acta2/Tagln/Rgs5 signature: Despite their transcriptional similarity to smooth muscle cells, pericytes formed a distinct cluster with a clear marker gene signature.
MyGene conversion was critical: The dataset used Ensembl IDs instead of gene symbols, requiring automated conversion before any biological interpretation was possible.
Single-cell resolution reveals what bulk RNA-seq cannot: Rare populations like Epicardial Cells, Neural Cells and Progenitor Cells would have been completely invisible in bulk RNA-seq analysis.
Cloud infrastructure adds real scalability: The Nextflow + AWS Batch setup means this pipeline can be deployed on datasets 100x larger without any code changes, simply by adjusting resource parameters.

What's Next

Trajectory analysis — using RNA velocity or PAGA to map how progenitor cells differentiate into mature cardiac cell types over developmental time
Differential gene expression — comparing cell type proportions and gene expression between healthy and diseased cardiac samples
Full AWS Batch deployment — completing VPC and IAM configuration for end-to-end cloud execution
Pathway enrichment — running GO and KEGG enrichment on marker genes to understand biological processes active in each cell type
Integration with spatial transcriptomics — mapping scRNA-seq cell types onto spatial coordinates to understand their physical location in the heart

References

Scanpy: Wolf FA, Angerer P, Theis FJ. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19, 15. https://scanpy.readthedocs.io
Dataset E-MTAB-6173: Payne S, et al. EMBL-EBI Single Cell Expression Atlas. https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6173
AnnData: Virshup I, et al. (2021). anndata: Annotated data. https://anndata.readthedocs.io
Leiden Algorithm: Traag VA, Waltman L, van Eck NJ. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233.
Nextflow: Di Tommaso P, et al. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35, 316–319. https://nextflow.io
AWS Batch: Amazon Web Services. AWS Batch Documentation. https://aws.amazon.com/batch/
MyGene: Wu C, et al. (2013). BioGPS and MyGene.info. Nucleic Acids Research, 41, D561–D565.
Python 3.14: Python Software Foundation. https://www.python.org
Visual Studio Code: Microsoft. https://code.visualstudio.com

Project Structure

scRNA-Seq/
├── data/
│   ├── E-MTAB-6173.aggregated_filtered_counts.mtx           # Expression matrix (too large for GitHub)
│   ├── E-MTAB-6173.aggregated_filtered_counts.mtx_cols      # Cell barcodes
│   └── E-MTAB-6173.aggregated_filtered_counts.mtx_rows      # Gene names
├── notebooks/
│   └── scrna_cardiac.ipynb                                  # Full interactive analysis pipeline
├── pipeline/
│   ├── main.nf                                              # Nextflow pipeline (4 processes)
│   ├── nextflow.config                                      # Pipeline configuration & profiles
│   └── conf/
│       └── aws.config                                       # AWS Batch & S3 configuration
└── results/
    ├── qc_metrics.png            
    ├── pca_variance.png          
    ├── umap_qc.png               
    ├── umap_clusters.png         
    ├── umap_celltypes.png        
    ├── marker_genes_named.png    
    ├── dotplot_markers.png       
    └── violin_markers.png

License

This project is open source and available under the MIT License.

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