Biological Data Processing Method Implementations

Collection of computational biology and scientific computing methods implemented in Python.

Implemented Methods

Dimensionality Reduction

• Principal Component Analysis (PCA)
  • Standardization
  • Variance decomposition
  • Loadings interpretation

Missing Data Handling

• Distribution-based imputation
• k-Nearest Neighbors (kNN) imputation

Clustering

• Agglomerative hierarchical clustering
  • Euclidean distance
  • Average linkage
  • Dendrogram reconstruction

Signal Processing

• Watershed-inspired peak detection
• Discrete diffusion-based denoising

Chromatin Contact Matrix Normalization

• Vanilla coverage normalization
• ICE (Iterative Correction and Eigenvector decomposition)

Functional Enrichment

• Gene Ontology (GO) enrichment scoring
  • Evidence filtering
  • Qualifier filtering
  • Aspect separation (BP / CC / MF)

Structural Bioinformatics

• Molecular dynamics trajectory analysis
  • RMSD
  • Radius of gyration
  • SASA
  • Radial distribution functions
  • Contact frequency analysis

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Reproducibility

• Notebooks run top-to-bottom without hidden state.
• Example datasets are provided under data/examples/ when possible.
• Large external datasets are not redistributed.
• Environment reproducibility is managed via pyproject.toml and optional module extras.

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Quickstart

git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>

python -m venv .venv
source .venv/bin/activate   # macOS / Linux
# .venv\Scripts\activate  # Windows

pip install -e ".[all]"
pip install jupyterlab

jupyter lab

Open notebooks from the notebooks/ directory.