Single-cell RNA-seq Analysis of Fibroblast Heterogeneity

Inspiration

Fibrotic skin diseases like Keloid represent a major clinical challenge due to excessive fibroblast proliferation and extracellular matrix accumulation. Understanding fibroblast heterogeneity can reveal key biological insights and potential therapeutic targets.

What We Did

We analyzed GSE163973, a single-cell RNA-seq dataset of Keloid and normal scar fibroblasts, identifying distinct subpopulations and their distribution across samples. PCA analysis was used to capture cell heterogeneity in 2D and interactive 3D plots. A PDF report summarizing the dataset, analysis, and visualizations was also generated.

Challenges

Working with high-dimensional scRNA-seq data and merging metadata across samples.

Ensuring reproducibility and clarity in automated report generation.

Key Features

Identification of fibroblast subpopulations in Keloid and normal scar tissue.

PCA analysis to visualize heterogeneity.

2D static plots and interactive 3D simulation.

Automated PDF report for easy sharing and presentation.

Built With

• 2d
• 3d
• and
• data
• fpdf
• matplotlib
• pandas
• pca
• python
• scikit-learn
• scrna-seq