GCalignR: An R package for aligning gas-chromatography data for ecological and evolutionary studies
GCalignR: An R package for aligning gas-chromatography data for ecological and evolutionary studies
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DOI：
10.1371/journal.pone.0198311
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发表时间：
2018-06-07
期刊：
影响因子：
3.7
通讯作者：
Hoffman, Joseph I.
中科院分区：
文献类型：
--
作者：
Ottensmann, Meinolf;Stoffel, Martin A.;Hoffman, Joseph I.
化学暗示可以说是动物交流的最基本手段，在配偶选择和亲属识别中起着重要作用。因此，人们越来越感兴趣的是使用气相色谱（GC）来研究生态进化相互作用的化学基础。GC-MS（质谱法）和FID（火焰离子化检测）通常用于鉴定生物样品（如皮肤拭子）的化学成分。所得色谱图包含根据其保留时间分离的峰，这些峰代表不同的物质。在不同样品的色谱图中，预计同源物质在相似的保留时间下保留。然而，随机且通常不可避免的实验变化会引入噪声，使得同源峰的对齐具有挑战性，特别是在缺少质谱数据的情况下使用GC-FID数据。在这里，我们提出了GC-FID R，一个用户友好的R软件包，用于根据保留时间对齐GC-FID数据。该软件包是专门为生态和进化研究而开发的，这些研究旨在调查多个通常高度可变的生物样本的相似性模式，例如代表不同性别，年龄组或生殖阶段。该软件包还实现了动态可视化，以便于检查和微调所产生的比对，并可以集成到R中更广泛的工作流程中，以促进下游的多变量分析。我们展示了一个示例工作流程，使用南极海狗的经验数据，并通过计算多个数据集的对齐错误率来探索用户定义的参数值的影响。所得到的比对对于大多数探索的参数空间具有较低的错误率，并且我们还可以表明，GCCRR的表现与其他可用软件一样好或更好。我们希望GCSTR将有助于简化化学数据集的处理，并提高动物化学通讯和相关领域研究中化学分析的标准化和可重复性。
Chemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, there is growing interest in the use of gas chromatography (GC) to investigate the chemical basis of eco-evolutionary interactions. Both GC-MS (mass spectrometry) and FID (flame ionization detection) are commonly used to characterise the chemical composition of biological samples such as skin swabs. The resulting chromatograms comprise peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we present GCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package was developed specifically for ecological and evolutionary studies that seek to investigate similarity patterns across multiple and often highly variable biological samples, for example representing different sexes, age classes or reproductive stages. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show that GCalignR performed equally well or better than other available software. We hope that GCalignR will help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.