Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis.
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis.
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DOI：
10.1186/2049-2618-2-15
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发表时间：
2014
期刊：
影响因子：
15.5
通讯作者：
Gloor GB
中科院分区：
文献类型：
--
作者：
Fernandes AD;Reid JN;Macklaim JM;McMurrough TA;Edgell DR;Gloor GB
利用高通量测序生成数据集的实验设计包括RNA测序（RNA-seq）、染色质免疫沉淀测序（ChIP-seq）、16 S rRNA基因片段测序、宏基因组分析和选择性生长实验。在每种情况下，基础数据是相似的，并且由映射到每个样品中的大量特征的测序读数的计数组成。尽管存在这种潜在的相似性，但用于这些实验设计的数据分析方法都是不同的，并且不会在实验之间转换。在物理和地质科学中已经开发了替代方法，将类似的数据视为组合。成分数据分析方法将数据转换为相对丰度，结果是分析更稳健和可重复。来自体外选择性生长实验、RNA-seq实验和人类微生物组计划16 S rRNA基因丰度数据集的数据通过ALDEx 2进行检查，ALDEx 2是一种成分数据分析工具，使用贝叶斯方法推断技术和统计错误。ALDEx 2方法被证明适用于所有三种类型的数据：它正确地识别了差异生长实验中特征的方向和差异丰度，它识别了RNA-seq数据集中基本相似的差异表达基因集作为主要工具，并且它识别了人类微生物组项目数据集中区分舌背和颊粘膜的分类群。ALDEx 2的设计减少了由少数样本中的许多特征组成的数据集所导致的假阳性识别的数量。由每个特征计数组成的高通量测序数据集的统计分析表明，ALDEx 2 R软件包是一种简单而强大的工具，可应用于RNA-seq，16 S rRNA基因测序和差异生长数据集，并扩展到使用类似方法的其他技术。
Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible. Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples. Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.
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影响因子：
3.7
作者：
La Rosa PS;Brooks JP;Deych E;Boone EL;Edwards DJ;Wang Q;Sodergren E;Weinstock G;Shannon WD
通讯作者：
Shannon WD
影响因子：
3
作者：
Hardcastle TJ;Kelly KA
通讯作者：
Kelly KA
影响因子：
4.3
作者：
Faust K;Sathirapongsasuti JF;Izard J;Segata N;Gevers D;Raes J;Huttenhower C
通讯作者：
Huttenhower C
DOI：
10.2307/2987937
发表时间：
1983-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN
影响因子：
--
作者：
ALTMAN, DG;BLAND, JM
通讯作者：
BLAND, JM
DOI：
10.2202/1544-6115.1627
发表时间：
2011-01-01
影响因子：
0.9
作者：
Auer, Paul L.;Doerge, Rebecca W.
通讯作者：
Doerge, Rebecca W.