标题： HybridQC：用于单细胞RNA测序数据的机器学习增强质量控制 显示英文标题

标题： HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data

摘要： HybridQC 是一个 R 包，通过结合传统的基于阈值的过滤和基于机器学习的异常值检测，简化了单细胞 RNA 测序（scRNA-seq）数据的质量控制（QC）。它提供了一个高效且自适应的框架，使用诸如孤立森林等技术，在噪声或浅深度数据集中识别低质量细胞，同时与广泛采用的格式如 Seurat 对象兼容。该包轻量级，易于安装，适用于研究环境中的小到中型 scRNA-seq 数据集。HybridQC 特别适用于涉及非模式生物、稀有样本或试点研究的项目，其中自动化和灵活的 QC 对于可重复性和下游分析至关重要。 显示英文摘要

摘要： HybridQC is an R package that streamlines quality control (QC) of single-cell RNA sequencing (scRNA-seq) data by combining traditional threshold-based filtering with machine learning-based outlier detection. It provides an efficient and adaptive framework to identify low-quality cells in noisy or shallow-depth datasets using techniques such as Isolation Forest, while remaining compatible with widely adopted formats such as Seurat objects. The package is lightweight, easy to install, and suitable for small-to-medium scRNA-seq datasets in research settings. HybridQC is especially useful for projects involving non-model organisms, rare samples, or pilot studies, where automated and flexible QC is critical for reproducibility and downstream analysis.