PsiNorm: a scalable normalization for single-cell RNA-seq data.
PsiNorm: a scalable normalization for single-cell RNA-seq data.
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DOI：
10.1093/bioinformatics/btab641
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发表时间：
2021-12-22
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影响因子：
--
通讯作者：
Romualdi C
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文献类型：
--
作者：
Borella M;Martello G;Risso D;Romualdi C
单细胞RNA测序（scRNA-seq）能够以单细胞分辨率进行全转录组基因表达测量，从而全面了解组织和生物体发育的组成和动态。scRNA-seq方案的发展已经导致细胞通量的急剧增加，加剧了先前对于批量测序出现的许多计算和统计问题。特别是，对于scRNA-seq数据，所有分析步骤（包括标准化）在内存使用和计算时间方面都变得计算密集。从这个角度来看，需要能够有效扩展的新的精确方法。在这里，我们提出了PsiNorm，一种基于幂律Pareto分布参数估计的样本间归一化方法。在这里，我们表明帕累托分布与scRNA-seq数据非常相似，特别是那些来自使用独特分子标识符的平台的数据。受此结果的启发，我们实现了PsiNorm，这是一种简单且高度可扩展的归一化方法。我们基准PsiNorm对其他七种方法的集群识别，一致性和所需的计算资源。我们证明了PsiNorm是表现最好的方法之一，在准确性和可扩展性之间表现出良好的权衡。此外，PsiNorm不需要参考，这一特性使其在监督分类设置中非常有用，其中需要对新的样本外数据进行归一化。 PsiNorm在scone Bioconductor包中实现，可在https://bioconductor.org/packages/scone/上获得。 补充数据可在Bioinformatics在线获得。
Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable. Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized. PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/. Supplementary data are available at Bioinformatics online.
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