DOI: 10.5281/zenodo.18797680
title: Distinct Long Non-coding RNA Regulatory Architectures Encode Lineage Stability and Epithelial Plasticity

description: # lncRNA Regulatory Architecture Study

**Distinct Long Non-coding RNA Regulatory Architectures Encode Lineage Stability and Epithelial Plasticity**

**Author:** Neetu Singh, PhD  **Affiliation:** Associate Professor, Molecular Biology Unit, Center for Advance Research,  King George's Medical University, Lucknow-226 003, Uttar Pradesh, India  **Contact:** neetusingh@kgmcindia.edu  **ORCID:** [add your ORCID here]

> **Manuscript DOI:** [add after acceptance]  > **Processed Data (Zenodo):** [https://doi.org/10.5281/zenodo.XXXXXXX](https://doi.org/10.5281/zenodo.XXXXXXX)  > **Code Archive (Zenodo):** [https://doi.org/10.5281/zenodo.YYYYYYY](https://doi.org/10.5281/zenodo.YYYYYYY)

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## Overview

This repository contains the complete analysis pipeline for an integrative study of ~70,500 annotated lncRNA transcripts across hematopoietic, brain, endometrial, and breast tissues profiled on the Affymetrix Human Transcriptome Array 2.0 (HTA-2.0, GPL17586).

**Core findings:**- Hematopoietic, brain, and endometrial samples form compact lineage-resolved PCA clusters (stable regulatory programs)- Breast samples show broad dispersion and high loading entropy (distributed, plastic regulatory architecture)- Only 33 lncRNAs are shared between brain and endometrium; no overlap across all four lineages- Cohort-imbalance controls confirmed hematopoietic PC1 dominance is biological, not artefactual (permutation p < 0.001)

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## Repository Structure

```lncrna-lineage-architecture/├── scripts/│   ├── 01_preprocessing.py          # Merge GEO CSVs → KNN imputation → z-score│   ├── 02_pca_analysis.py           # PCA + lineage lncRNA selection + cohort controls│   ├── 03_entropy_upset_heatmap.py  # Shannon entropy (bootstrapped CI) + UpSet + heatmap│   ├── 04_umap_batch_assessment.py  # UMAP batch-effect visualisation (Supp. Fig. S14)│   ├── download_geo_data.sh         # Auto-download raw data from NCBI GEO│   └── requirements.txt             # Python package dependencies│├── data/│   ├── raw/          # Raw GEO expression CSVs (NOT tracked — download from GEO)│   └── processed/    # Processed matrices (available on Zenodo — link above)│├── results/│   ├── pca/          # PCA scores, loadings, lineage probe-set lists, stat tests│   ├── entropy/      # Entropy scores with bootstrapped CIs│   ├── umap/         # UMAP coordinates│   └── figures/      # All manuscript figures (600 dpi PNG + vector PDF)│├── supplementary/│   └── Supplementary_Tables_S1_S4.xlsx  # Lineage-associated lncRNA lists│├── .gitignore├── LICENSE└── README.md```

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## Source Data (NCBI GEO)

All raw expression data are publicly available. Replace `GSE######` with real accessions:

| GEO Accession | Tissue       | Condition | Platform | n Samples ||---------------|-------------|-----------|----------|-----------|| GSE######     | Hematopoietic | MDS     | GPL17586 | ~207      || GSE######     | Breast       | Tumour    | GPL17586 | ~75       || GSE######     | Breast       | Normal    | GPL17586 | ~13       || GSE######     | Brain        | Tumour    | GPL17586 | ~7        || GSE######     | Brain        | Normal    | GPL17586 | ~2        || GSE######     | Endometrium  | Benign    | GPL17586 | ~8        |

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## Processed Data (Zenodo)

The four processed files required to reproduce downstream analyses are deposited at:

**https://doi.org/10.5281/zenodo.XXXXXXX**

| File | Description ||------|-------------|| `expression_matrix_imputed.csv` | Merged HTA-2.0 matrix after KNN imputation (k=5) || `expression_matrix_zscore.csv`  | Z-scored matrix — input to all PCA analyses || `sample_metadata.csv`           | Sample annotations with GEO accessions || `probe_manifest.csv`            | Retained probe set IDs (~70,500) |

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## Reproducing the Analysis

### Setup

```bashgit clone https://github.com/YourUsername/lncrna-lineage-architecture.gitcd lncrna-lineage-architecture

python -m venv venvsource venv/bin/activate        # Windows: venv\Scripts\activatepip install -r scripts/requirements.txt```

### Option A — Use processed data from Zenodo (fastest)

```bashmkdir -p data/processed# Download the 4 CSV files from https://doi.org/10.5281/zenodo.XXXXXXX# Place them in data/processed/```

### Option B — Reproduce from raw GEO data

```bash# 1. Edit download_geo_data.sh — replace all GSE###### with real accessions# 2. Run the downloaderbash scripts/download_geo_data.sh

# 3. Run preprocessingpython scripts/01_preprocessing.py```

### Run the full pipeline

```bashpython scripts/02_pca_analysis.py          # PCA + cohort controls + Levene's testpython scripts/03_entropy_upset_heatmap.py # Entropy + UpSet + heatmappython scripts/04_umap_batch_assessment.py # UMAP batch assessment```

All figures are saved to `results/figures/` as both PNG (600 dpi) and PDF.

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## Analysis Parameters

| Parameter | Value | Reference ||-----------|-------|-----------|| KNN k | 5 | Supp. Method S6 || Loading threshold | Top 2% (~1,410 probesets) | Supp. Method S1 || Bootstrap iterations | 1,000 | Supp. Method S4 || Permutation iterations | 1,000 | Supp. Method S4 || Down-sampling iterations | 100 | Supp. Method S4 || UMAP n_neighbors | 15 | Supp. Method S5 || UMAP min_dist | 0.1 | Supp. Method S5 || Random seed | 42 | Throughout |

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## Citation

```bibtex@article{Singh2025lncRNA,  author  = {Singh, Neetu},  title   = {Distinct Long Non-coding RNA Regulatory Architectures             Encode Lineage Stability and Epithelial Plasticity},  journal = {[Journal]},  year    = {2025},  doi     = {[pending]}}```

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## License

Code: [MIT License](LICENSE)  Processed data (Zenodo): [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)