HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909683963396096 |
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| author | Lai, Kaitao |
| author_facet | Lai, Kaitao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data Lai, Kaitao Genomics 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. |
| title | HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data |
| topic | Genomics |
| url | https://arxiv.org/abs/2507.08058 |