Transcriptome diversity is a systematic source of variation in RNA-sequencing data.
Transcriptome diversity is a systematic source of variation in RNA-sequencing data.
复制标题
DOI：
10.1371/journal.pcbi.1009939
复制
发表时间：
2022-03
影响因子：
4.3
通讯作者：
Fraser HB
中科院分区：
文献类型：
--
作者：
García-Nieto PE;Wang B;Fraser HB
RNA测序已被广泛用作探测基因表达的重要工具。虽然已经建立了分析RNA-SEQ数据的标准做法，但解释和消除人工信号仍然是具有挑战性的。已经发现，性别、年龄、批次和测序技术等几个生物和技术因素会对这些估计产生偏差。表达残差概率估计(PEER)在基因表达测量中推断出广泛的方差分量，已被用来解释一些系统效应，但解释这些PEER因素仍然具有挑战性。在这里，我们表明转录组多样性-一个基于香农熵的简单度量-解释了基因表达的很大一部分可变性，并且是在同行因子中编码的最强的已知因素。然后，我们表明转录组多样性与不同生物体和数据集的多个技术和生物变量有显着关联。总而言之，转录组多样性为基因表达估计和同行协变量的主要变异来源提供了一个简单的解释。尽管每个生物体中的细胞都有几乎相同的DNA序列，但它们在功能上有很大的不同--例如，神经元与肌肉细胞非常不同。这在很大程度上是因为不同的基因从DNA转录成RNA，这是被称为基因表达的过程中的关键步骤。RNA水平的测量是生物学研究中的重要工具，但由于许多潜在的混杂因素而变得复杂。为了解释这一点，计算方法可以修正未知的混杂因素，但这些方法并不提供关于这些混杂因素是什么的任何信息。在这里，我们展示了转录组多样性--一种基于香农熵的简单衡量标准--解释了基因表达测量中的很大一部分变异性，以及领先方法检测到的混杂因素。这一流行因素为基因表达估计中的主要变异来源提供了一个简单的解释。
RNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to interpret and remove artifactual signals. Several biological and technical factors such as sex, age, batches, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER), which infers broad variance components in gene expression measurements, has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors. Here we show that transcriptome diversity–a simple metric based on Shannon entropy–explains a large portion of variability in gene expression and is the strongest known factor encoded in PEER factors. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. In sum, transcriptome diversity provides a simple explanation for a major source of variation in both gene expression estimates and PEER covariates. Although the cells in every individual organism have nearly identical DNA sequences, they differ substantially in their function—for instance, neurons are very different from muscle cells. This is in large part because different genes are transcribed from DNA into RNA, a key step in the process known as gene expression. The measurement of RNA levels is an important tool in studying biology, but is complicated by many potentially confounding factors. To account for this, computational methods can correct for unknown confounders, but these do not provide any information about what these confounders are. Here we show that transcriptome diversity–a simple metric based on Shannon entropy–explains a large portion of variability in both gene expression measurements as well as the confounding factors detected by a leading method. This prevalent factor provides a simple explanation for a primary source of variation in gene expression estimates.
登录
查看更多内容
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
4.6
作者：
Arora, Sonali;Pattwell, Siobhan S.;Bolouri, Hamid
通讯作者：
Bolouri, Hamid
影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
影响因子：
4.4
作者：
Catalán A;Hutter S;Parsch J
通讯作者：
Parsch J
影响因子：
14.9
作者：
McCarthy DJ;Chen Y;Smyth GK
通讯作者：
Smyth GK