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arXiv · 2507.08058

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

Kaitao Lai
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Abstract

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.

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Kaitao Lai. 2025-07-10. HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data. https://arxiv.org/abs/2507.08058

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