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This article details SCR-MF, a novel computational framework that integrates scRecover and Random Forests to address dropout events in single-cell RNA sequencing data. By combining principled detection with non-parametric imputation, the method offers a transparent and efficient solution for preserving biological fidelity in mid-scale genomic datasets. While the application is specific to bioinformatics, the core contribution lies in advancing machine learning techniques for handling sparse, high-dimensional data structures.
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
arXiv:2511.16923v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
arXiv:2511.16923 [cs.LG]
(or arXiv:2511.16923v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2511.16923 Focus to learn more arXiv-issued DOI via DataCite
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