A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
Abstract
Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable performance comparable to or exceeding existing imputation methods in most cases, while preserving biological fidelity and transparency. Runtime analysis demonstrates that SCR-MF provides a competitive balance between accuracy and computational efficiency, making it suitable for mid-scale single-cell datasets.
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Anaissi, A., Liu, D., Jia, Y., Huang, W., Alyassine, W., Akram, J. (2025). A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests. arXiv preprint arXiv:2511.16923.
Ali Anaissi, Deshao Liu, Yuanzhe Jia, Weidong Huang, Widad Alyassine, and Junaid Akram. "A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests." arXiv preprint arXiv:2511.16923 (2025).