• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Genetics >A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data
【2h】

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

机译：利用杠杆信噪比（LSTNR）方法从嘈杂和低复制RNAseq数据中提取差异表达的基因和表达的多变量模式

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays.
机译：对生命科学家而言，RNAseq（一种用于评估基因表达水平变化的下一代测序工具）的一项重要功能在于其前所未有的分辨率。它可以一次对成千上万个基因之间以及实验组之间的转录数量进行计分。但是，其高昂的成本将实验设计限制在非常小的样本量（通常为N = 3）上，这通常会导致统计不足的分析能力和可重复性差。所有这些问题都伴随着实验噪声的存在，当样本量有限（例如，小规模的先导试验），实验群体表现出生物学上的异质或扩散表达表型（例如，患者样本）时，很难将其与仪器误差区分开来。 ，或在密切相关的实验条件（例如毒理作用模式或MOA）的转录特征之间进行区分。在这里，我们提出一种杠杆式信噪比（LSTNR）阈值化方法，该方法基于对齐的读取检测限的广义线性建模（GLM），从嘈杂的低复制RNAseq数据中提取差异表达的基因（DEG）。 LSTNR方法使用独立的不可知论过滤策略来定义每个基因检测到的总读取计数的动态范围，并分配统计权重，以便在差异表达分析中对具有更好测序分辨率的基因进行优先排序。为了评估其性能，我们实施了LSTNR方法来分析三个单独的数据集：首先，使用系统噪声较高的计算机模拟数据集，我们证明了LSTNR可以提取预先设计的表达模式并区分“噪声”和“真实”差异表达假基因成功率为100％；然后，我们说明了LSTNR方法如何将患者来源的乳腺癌标本正确地分配给四种报道的分子亚型（腔A，腔B，Her2富集和基底样）之一。最后，我们展示了使用LSTNR方法检索在三种营养途径下接触三种有毒物质的大鼠肝脏中引起的五种不同作用方式（MOA）的能力。通过将差分测量与解析能力相结合来检测DEG，LSTNR方法提供了一种可替代的方法来查询嘈杂的和低重复性的RNAseq数据集，该数据集可同时处理多种生物学条件，并定义了基准以通过标准台式测定法验证RNAseq实验。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号