Computational Cell Reprogramming Review — Reproduction Materials

Author: Paola Vera-Licona (veralicona@uchc.edu)
Affiliation: University of Connecticut School of Medicine, Farmington, CT, USA

Public reproduction package for the PLOS Computational Biology submission
Computational Cell Reprogramming: A Cross-Modality Framework for Cellular Intervention Design (Vera-Licona Research Group, 2026).

This repository contains the minimal scripts and data needed to regenerate:

Manuscript artifact	Folder	Output
Supporting Information S3 (reproducibility audit)	data/	SI_3_ReproducibilityAudit.xlsx
Figure 3	figure3/	Fig3_reproducibility.{pdf,png,tiff}
Figure 2	figure2/	Fig2_modality_upset.{pdf,png,tiff}
Supporting Information S4 (dataset reuse)	dataset_reuse/	SI_4_DatasetReuse.xlsx
Supporting Information S5 (bibliometric analysis)	bibliometric/	trend CSVs + summary plot

Quick start (Docker — recommended)

Requires Docker Desktop (or Docker Engine + Compose plugin).

# Build the image once
docker compose build

# Regenerate Figure 2, Figure 3, and SI_4 (offline, ~1 minute)
docker compose run --rm run-offline

# Bibliometric analysis (network + your NCBI email; ~10–20 minutes)
NCBI_EMAIL=you@institution.edu docker compose run --rm run-bibliometric

# JupyterLab with guided notebooks
docker compose up jupyter
# Open http://localhost:8888 and open notebooks/00_Run_All_Offline.ipynb

Outputs are written into this repository folder (mounted as a volume), so they persist after the container exits.

Quick start (Jupyter notebooks)

From the repository root:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements-all.txt
jupyter lab notebooks/

Notebook	What it runs
notebooks/00_Run_All_Offline.ipynb	Figure 3 → Figure 2 → SI_4
notebooks/01_Figure3_Reproducibility.ipynb	Figure 3 only
notebooks/02_Figure2_ModalityUpSet.ipynb	Figure 2 only
notebooks/03_DatasetReuse_Table.ipynb	SI_4 xlsx only
notebooks/04_Bibliometric_Analysis.ipynb	SI_5 (set email first; needs network)

Notebooks call the same Python scripts as the command-line workflow — they are thin wrappers, not duplicate logic.

Quick start (command line)

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
bash scripts/run_offline.sh

For bibliometrics, also pip install -r bibliometric/requirements_bibliometric.txt, then:

NCBI_EMAIL=you@institution.edu bash scripts/run_bibliometric.sh

Individual scripts

Figure 2

cd figure2 && python fig2_modality_upset.py

Figure 3

cd figure3 && python fig3_reproducibility.py

Reads data/SI_3_ReproducibilityAudit.xlsx. Trip-wire asserts verify headline Section 5 counts (27 runnable, 43 with code, 14 FAIR4RS ≥ 3).

Supporting Information S4

cd dataset_reuse && python build_si_4_xlsx.py

Supporting Information S5

cd bibliometric
python bibliometric_analysis.py \
  --email YOUR_EMAIL@institution.edu \
  --start-year 2014 \
  --end-year 2026 \
  --output-dir results

PubMed/PMC counts may drift slightly as new papers are indexed.

Repository layout

CellReprogrammingReview2026/
├── data/                         # Supporting Information S3 spreadsheet
├── notebooks/                    # Guided Jupyter notebooks
├── scripts/                      # run_offline.sh, run_bibliometric.sh
├── reproducibility_audit/        # audit notes + optional xlsx legend patch
├── figure2/                      # Figure 2 scripts + reference map
├── figure3/                      # Figure 3 script
├── dataset_reuse/                # SI_4 builder + input CSVs
├── bibliometric/                 # SI_5 PubMed/PMC queries
├── Dockerfile
├── docker-compose.yml
├── requirements.txt              # core deps (figures + SI_4)
├── requirements-all.txt          # core + bibliometric + Jupyter
└── LICENSE                       # MIT (code)

Licenses

• Code (Python scripts, notebooks, Docker files): MIT License
• Data (CSV/xlsx tables in data/ and dataset_reuse/data/): CC BY 4.0

Citation

If you use these materials, please cite the PLOS Computational Biology article (DOI to be added upon acceptance) and this repository:

Vera-Licona, P. (2026). Computational Cell Reprogramming Review — Reproduction Materials. GitHub: VeraLiconaResearchGroup/CellReprogrammingReview2026.