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.
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.
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DOI：
10.3389/fgene.2018.00176
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发表时间：
2018
影响因子：
3.7
通讯作者：
Woychik RP
中科院分区：
文献类型：
--
作者：
Lozoya OA;Santos JH;Woychik RP
对于生命科学家来说，用于估计基因表达水平变化的下一代测序工具RNAseq提供的一个重要功能在于其前所未有的分辨率。它可以同时在数千个基因之间和实验组之间的转录本数量上进行计数。然而，它的高成本限制了实验设计到非常小的样本量，通常N = 3，这往往导致统计学上的动力不足的分析和再现性差。所有这些问题都因实验噪音的存在而变得更加复杂，当样本量有限时，实验噪音更难与仪器误差区分开来（例如，小预算中试），实验群体表现出生物学异质性或弥散性表达表型（例如，患者样品），或者当在密切相关的实验条件的转录标记之间进行区分时（例如，毒理学作用模式或MOAs）。在这里，我们提出了一种杠杆信噪比（LSTNR）阈值方法，建立在对齐读检测限的广义线性模型（GLM）基础上，从嘈杂的低复制RNAseq数据中提取差异表达基因（DEG）。LSTNR方法使用不可知的独立过滤策略来定义每个基因检测到的聚合读段计数的动态范围，并分配统计权重，该统计权重在差异表达分析中优先考虑具有更好测序分辨率的基因。为了评估其性能，我们实施了LSTNR方法来分析三个独立的数据集：首先，使用系统性噪声的计算机数据集，我们证明了LSTNR可以以100%的成功率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;然后，我们举例说明了LSTNR方法如何将患者来源的乳腺癌标本正确地分配到四种已报告的分子亚型中的一种（鲁米诺A、鲁米诺B、Her 2富集和基底样）;最后，我们通过使用LSTNR方法显示了在暴露于三种毒物的大鼠肝脏中在三种营养途径下引起的五种不同作用模式（MOA）的检索能力。通过将差分测量与分辨能力相结合来检测DEG，LSTNR方法提供了一种替代方法来询问有噪声和低复制的RNAseq数据集，该方法可以同时处理多种生物条件，并定义了使用标准台式试验验证RNAseq实验的基准。
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.
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影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
12.3
作者：
Frietze S;Wang R;Yao L;Tak YG;Ye Z;Gaddis M;Witt H;Farnham PJ;Jin VX
通讯作者：
Jin VX
影响因子：
4.7
作者：
Clawson, GA;Benedict, CM;Weisz, J
通讯作者：
Weisz, J
影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.
影响因子：
3.5
作者：
Ekstrand, MI;Falkenberg, M;Larsson, NG
通讯作者：
Larsson, NG