Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA.
Bioinformatics. 2019 Dec 15;35(24):5306-5308. doi: 10.1093/bioinformatics/btz601.
Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.
scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.
Supplementary data are available at Bioinformatics online.
单细胞 RNA 测序是一种革命性的技术，可以用于描述细胞间转录组异质性。然而，由于基因表达通常受到技术伪影和真实生物变异的共同驱动，因此数据容易受到干扰。正确区分这两种效应对于防止虚假结果至关重要。虽然有一些工具可以检测和去除单个单细胞 RNA-seq 数据集中的低质量细胞，但缺乏一种方法来检查样本集之间的一致性，并检测系统偏差、批次效应和离群值。我们提出了 scRNABatchQC，这是一个 R 包，可以同时比较多个样本集的大量技术和生物学特征，这为区分技术伪影和生物变异提供了有价值的线索。scRNABatchQC 有助于识别和系统地表征单细胞转录组数据中的变异性来源。对数据集之间一致性的检查可以直观地检测偏差和离群值。
scRNABatchQC 可在 https://github.com/liuqivandy/scRNABatchQC 上免费获取，作为一个 R 包。
补充数据可在生物信息学在线获得。
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