Guidance for RNA-seq co-expression estimates: the importance of data normalization, batch effects, and correlation measures
Guidance for RNA-seq co-expression estimates: the importance of data normalization, batch effects, and correlation measures
复制标题
RNA-seq 共表达估计指南：数据标准化、批次效应和相关性测量的重要性
DOI：
10.1101/2021.03.11.435043
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Vandenbon Alexis
中科院分区：
文献类型：
--
作者：
Lee Jong-Hun;Saito Yutaka;Park Sung-Joon;Nakai Kenta;Vandenbon Alexis
基因共表达分析是一种有吸引力的工具，可以利用大量的公共RNA-seq数据集来预测基因功能和调控机制。然而，从如此大的数据集准确预测基因共表达的最佳数据处理步骤仍然不清楚。特别是批效应校正的重要性是understudied.ResultsWe处理RNA-seq数据的68人类和76小鼠细胞类型和组织使用50个不同的工作流程到7,200全基因组基因共表达网络。然后，我们对导致高质量共表达预测的因素进行了系统分析，重点是归一化，批量效应校正和相关性测量。我们证实了高样本数对于高质量预测的关键重要性。然而，选择合适的归一化方法并应用批量效应校正可以进一步提高共表达估计的质量，相当于样品增加>80%和>40%。在较大的数据集中，批量效应的去除相当于样本量的两倍以上。最后，Pearson相关性似乎更适合比斯皮尔曼相关性，除了较小的datasets.ConclusionA准确预测基因共表达的关键点是收集大量的样本。然而，注意数据归一化、批量效应和相关性度量可以显著提高共表达估计的质量。
MotivationGene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate prediction of gene co-expression from such large datasets remain unclear. Especially the importance of batch effect correction is understudied.ResultsWe processed RNA-seq data of 68 human and 76 mouse cell types and tissues using 50 different workflows into 7,200 genome-wide gene co-expression networks. We then conducted a systematic analysis of the factors that result in high-quality co-expression predictions, focusing on normalization, batch effect correction, and measure of correlation. We confirmed the key importance of high sample counts for high-quality predictions. However, choosing a suitable normalization approach and applying batch effect correction can further improve the quality of co-expression estimates, equivalent to a >80% and >40% increase in samples. In larger datasets, batch effect removal was equivalent to a more than doubling of the sample size. Finally, Pearson correlation appears more suitable than Spearman correlation, except for smaller datasets.ConclusionA key point for accurate prediction of gene co-expression is the collection of many samples. However, paying attention to data normalization, batch effects, and the measure of correlation can significantly improve the quality of co-expression estimates.