CEP-IP: An Explainable Framework for Subpopulation Identification

🧬CEP-IP: An Explainable Framework for Cell Subpopulation Identification in Single-cell Transcriptomics (Supplementary Tables 1-13) Author: Kah Keng Wong Publication date: 15 September 2025 Preprint: [arXiv:2509.12073]DOI: [10.5281/zenodo.17114394] 📋Description• This record provides Supplementary Tables 1–13 (Excel format) for the preprint "CEP-IP: An Explainable Framework for Cell Subpopulation Identification in Single-cell Transcriptomics" by Kah Keng Wong (2025), published on arXiv [arXiv:2509.12073] • The study employs generalized additive model (GAM) with REML and TPRS to explore TRPM4 and ribosomal gene interactions in single-cell RNA-seq (scRNA-seq) data from prostate cancer (PCa) patients, identifying cell subpopulations via the CEP-IP framework with therapeutic potential. • The tables, derived from the processed scRNA-seq dataset [GEO: GSE185344], include: i) Quality Control and Clustering (Supplementary Tables 1–2) ii) Gene Selection and Enrichment (Supplementary Tables 3–5) iii) GAM Modeling and Optimization (Supplementary Tables 6–10) iv) Cell Classification and Enrichment (Supplementary Tables 11–13) 🎯CitationIf using these tables, please cite: Wong KK (2025). CEP-IP: An Explainable Framework for Cell Subpopulation Identification in Single-cell Transcriptomics. arXiv preprint [arXiv:2509.12073] Also cite the source dataset: Wong HY, Sheng Q, Hesterberg AB, Croessmann S et al. (2022). Single cell analysis of cribriform prostate cancer reveals cell intrinsic and tumor microenvironmental pathways of aggressive disease. Nat Commun 13(1):6036. https://doi.org/10.1038/s41467-022-33780-1 🧾LicenseThe tables are licensed under the [MIT License] 🔎Related Resources• Preprint: [arXiv:2509.12073]• Processed dataset: [Hugging Face: kahkengwong/CEP-IP_Framework]• Code: [GitHub: kahkengwong/CEP-IP_Framework]• Source dataset: [GEO: GSE185344]