CEP-IP: An Explainable Framework for Cell Subpopulation Identification in Single-cell Transcriptomics

🧬CEP-IP: An Explainable Framework for Cell Subpopulation Identification in Single-cell Transcriptomics (Supplementary Tables 1-17) [Published in Computer Methods and Programs in Biomedicine]

Author: Kah Keng Wong  Publication date: 31 March 2026Published: [Computer Methods and Programs in Biomedicine, 282 (2026) 109372]DOI: [10.5281/zenodo.17114394]

🔬 Abstract

Background and objective: Single-cell RNA sequencing (scRNA-seq) frameworks lack explainable approaches for identifying cell subpopulations harboring strong pairwise monotonic gene-module relationships between a gene of interest (GOI) and its co-expressed genes. In this study, CEP-IP is introduced as a novel explainable machine learning framework to address this gap.

Methods: Prostate cancer (PCa) scRNA-seq dataset was used as the initial dataset, whereby TRPM4 served as the GOI and its co-expressed ribosomal genes (Ribo) were identified via Spearman-Kendall dual-filter (i.e., dual-filtered genes, DFG). Next, generalized additive modeling quantified the strength of TRPM4-Ribo relationship, represented by deviance explained (DE). TRPM4-Ribo’s DE was then assigned to individual cells via cell explanatory power (CEP) classification, identifying cells harboring the TRPM4-Ribo module [i.e., top-ranked explanatory power (TREP) cells]. TRPM4-Ribo transcriptional space was then stratified into pre-IP and post-IP regions using inflection point (IP) analysis, producing four distinct cell subpopulations per patient for pathway analysis. Validation was performed in the Allen middle temporal gyrus (MTG) and Neftel glioblastoma multiforme (GBM) transcriptomically heterogeneous datasets.

Results: TRPM4-Ribo modeling outperformed alternative gene set modules (FDR<0.05). In each PCa patient, CEP-IP yielded four cell subpopulations, where pre-IP TREP cells showed enrichment of immune-related processes, and post-IP TREP cells were enriched for ribosomal, translation, and cell adhesion pathways. In the MTG validation dataset (CARM1P1-DFG module), post-IP TREP cells showed enrichment of neuron projection ontologies. In the GBM dataset, FOXM1 was the sole GOI yielding mesenchymal-state DFG, with FOXM1-DFG post-IP TREP cells enriched for cell division and microtubule pathways; 3D trajectory analysis demonstrated continuous trajectories of TREP cells that were obscured in 2D embeddings.

Conclusions: CEP-IP identifies biologically distinct cell subpopulations in three independent scRNA-seq datasets. The framework may generalize to other pairwise GOI-DFG module in single-cell transcriptomics beyond the datasets investigated in this study.

📋Description• This record provides Supplementary Tables 1–17 (Excel format) for "CEP-IP: An explainable framework for cell subpopulation identification in single-cell transcriptomics" by Kah Keng Wong, published in Computer Methods and Programs in Biomedicine, 282 (2026) 109372 [doi:10.1016/j.cmpb.2026.109372]

• The supplementary tables 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 11–14)     v) Validation Analyses (Supplementary Tables 15–17)

🎯CitationIf using these tables, please cite:  Wong KK. CEP-IP: An explainable framework for cell subpopulation identification in single-cell transcriptomics. Computer Methods and Programs in Biomedicine, 282 (2026) 109372. https://doi.org/10.1016/j.cmpb.2026.109372

Also cite the source dataset:  H.Y. Wong, Q. Sheng, A.B. Hesterberg, S. Croessmann, B.L. Rios, et al. Single cell analysis of cribriform prostate cancer reveals cell intrinsic and tumor microenvironmental pathways of aggressive disease. Nature Communications, 13 (2022) 6036.https://doi.org/10.1038/s41467-022-33780-1

R.D. Hodge, T.E. Bakken, J.A. Miller, K.A. Smith, E.R. Barkan, et al. Conserved cell types with divergent features in human versus mouse cortex. Nature, 573 (2019) 61-68.https://doi.org/10.1038/s41586-019-1506-7

C. Neftel, J. Laffy, M.G. Filbin, T. Hara, M.E. Shore, et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell, 178 (2019) 835-849 e821.https://doi.org/10.1016/j.cell.2019.06.024

🧾LicenseThe tables are licensed under the [MIT License]

🔎Related Resources• Published paper: [Computer Methods and Programs in Biomedicine, 282 (2026) 109372]• Processed dataset: [Hugging Face: kahkengwong/CEP-IP_Framework]• Code: [GitHub: kahkengwong/CEP-IP_Framework]• Source dataset: [GEO: GSE185344]; [Allen MTG]; [Neftel GBM]