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.
Cite
@article{arxiv.2511.16923,
title = {A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests},
author = {Ali Anaissi and Deshao Liu and Yuanzhe Jia and Weidong Huang and Widad Alyassine and Junaid Akram},
journal= {arXiv preprint arXiv:2511.16923},
year = {2025}
}