小柯机器人

瑞士巴塞尔大学Erik van Nimwegen研究团队的最新研究利用单细胞RNA-seq数据（scRNA-seq）对基因表达状态进行了贝叶斯推断。该项研究成果发表在2021年4月29日出版的《自然-生物技术》上。

基于推断出表达状态与校正生物学和测量采样噪声并能根据倍数变化来测量表达变化的最基本要求，研究人员研发了一种称为“Sanity”的贝叶斯归一化方法（转录活性的SAmpling-Noise校正推论）。Sanity可以直接从原始唯一 分子标识符（UMI）计数中估算表达值和相关的误差线，而无需任何可调参数。使用模拟的和实际的scRNA-seq数据集，研究人员发现Sanity在下游任务上的表现优于其他归一化方法，例如找到最近的相邻细胞和将细胞聚类为亚型。

此外，研究还发现系统地高估低表达基因的表达变异性并通过将数据映射到低维表征而引入的虚假相关性，其他方法会产生严重失真的数据模式。

据悉，尽管单细胞RNA-seq数据分析方法取得了重大进展，但对如何最佳地标准化此类数据仍存在争议。

附：英文原文

Title: Bayesian inference of gene expression states from single-cell RNA-seq data

Author: Jrmie Breda, Mihaela Zavolan, Erik van Nimwegen

Issue&Volume: 2021-04-29

Abstract: Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data. A Bayesian procedure overcomes challenges in single-cell RNA-seq data normalization.

DOI: 10.1038/s41587-021-00875-x

Source: https://www.nature.com/articles/s41587-021-00875-x

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