澳大利亚沃尔特和伊丽莎·霍尔医学研究所Terence P. Speed、Anthony T. Papenfuss、和Ramyar Molania研究组在研究中取得进展。他们的最新研究发现PRPS可去除大规模RNA测序数据(RNA-seq)中的非必要变异。相关论文于2022年9月15日发表在《自然-生物技术》杂志上。

使用来自癌症基因组图谱(TCGA)的RNA-seq数据，研究人员揭示了几种非必要变异的来源，并在此展示了这些来源如何明显影响各种下游分析，包括癌症亚型鉴定、基因表达与生存之间的关联以及基因共表达分析。研究人员提出了一种称为伪样本的伪复制(PRPS)策略，以适用于最近开发的标准化方法，名为去除不需要的变异III (RUV-III)，以去除由文库大小、肿瘤纯度和批次效应造成的TCGA RNA-seq数据变异。研究通过将其与几个TCGA RNA-seq数据集上的标准TCGA归一化进行比较来证明该方法的价值。带有PRPS的RUV-III可用于整合和归一化来自多个不同实验室或平台的大型转录组数据集。

据了解，准确识别和有效去除不需要的变异对于从RNA测序数据中获得有意义的生物学结果至关重要，尤其是当数据来自大型且复杂的研究时。

附：英文原文

Title: Removing unwanted variation from large-scale RNA sequencing data with PRPS

Author: Molania, Ramyar, Foroutan, Momeneh, Gagnon-Bartsch, Johann A., Gandolfo, Luke C., Jain, Aryan, Sinha, Abhishek, Olshansky, Gavriel, Dobrovic, Alexander, Papenfuss, Anthony T., Speed, Terence P.

Issue&Volume: 2022-09-15

Abstract: Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

DOI: 10.1038/s41587-022-01440-w

Source: https://www.nature.com/articles/s41587-022-01440-w