用scRecover+随机森林，精准修复单细胞测序的丢失数据
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
- 先用scRecover检测丢数位置，再用missForest填补
- 在多个公开和模拟数据集上表现优于或持平主流方法
- 兼顾准确率与速度，适合中等规模数据处理
单细胞RNA测序（scRNA-seq）可在细胞水平进行转录组分析，但普遍存在丢数现象，干扰生物信号识别。本文提出SCR-MF，一种模块化两阶段流程：首先利用scRecover进行基于原理的丢数检测，随后通过missForest实现稳健的非参数填补。在多个公开及模拟数据集上，SCR-MF在大多数情况下性能可媲美甚至超越现有方法，同时保持生物真实性与可解释性。运行时长分析表明，该方法在准确率与计算效率之间取得良好平衡，适用于中等规模单细胞数据集。
原文摘要 · Abstract (English)
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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