A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
This work addresses data quality issues in scRNA-seq analysis for researchers, but it is incremental as it combines existing techniques into a modular workflow.
The paper tackles the problem of dropout events in single-cell RNA sequencing data by introducing SCR-MF, a hybrid framework that integrates scRecover and random forests for imputation, achieving performance comparable to or exceeding existing methods while maintaining biological fidelity and computational efficiency.
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.