Data mining: A Large Scale Re-analysis of Designed Microarray Experiments

** 任意设计实验的管道 ** 几个NERC资助的项目一直在利用创新的，更有效的微阵列设计。这些设计使用标准的双通道平台，但避免使用参考样品。由此产生的设计比传统的基于参考的设计效率高出50%。这些设计的主要困难是商业微阵列分析软件不适合分析这样的实验。将开发一种替代的、免费提供的研究工具来处理这些以及传统设计的实验。* 能够处理样品可用性的复杂性。*一般来说，基因组实验-特别是在环境设置中-通常以特定的方式设计，这取决于基因组材料的可用性和测量平台的可用性的交集。在实践中，这意味着测量技术重复或生物样品，或者将生物样品合并到单个测量样品中。在进一步的分析中忽略这些困难-这是今天的标准做法-会导致有偏见和不准确的结果。基于线性混合效应模型的分析工具应能给出准确的结果，无论样品制备如何，无论是混合样品还是重复测量同一样品。* 将微分表达式的概念扩展到任意复杂的治疗分配。* NERC合作者在这个项目中考虑的实验都比两种治疗方法的初始比较复杂得多。例如，在Paterson项目（NE/D 000602/1）中，从同一宿主中采集了3个不同阶段的线虫样本，这些线虫可以是经过处理的，也可以是未经处理的。在Pottinger项目中（小额赠款MGF 107，分子遗传学设施指导委员会），在给予雄性和雌性两种不同剂量的雌激素后，测量了棘鱼大脑中的基因表达。一个复杂的问题是，它是未知的，是否表型雌性鱼与高剂量的雌激素治疗是女性或性别逆转的男性。在泰勒项目（NER/T/S/2002/00182和NE/C507696/1）中，两种鱼类的雄性和雌性暴露于不同剂量的两种不同的环境污染物，并在战略选择的发育时间点进行采样。在所有这些例子中，有一个复杂的析因处理结构，这使得成对比较既没有吸引力，也没有科学意义。新生物学的另一个方面是需要探索更微妙的表型。在Sneddon项目（NER/I/S/2001/00768，NE/C 000889/1，NE/C 002164/1）中，在微阵列上比较的表型有细微的不同-例如，失去优势挑战的大胆鳟鱼和沮丧的大胆鳟鱼。除了专家之外，这是所有人的能力范围之外的福尔斯。分析工具应该能够处理任意复杂的因子协变量结构，并产生有意义的结果，可以根据这些协变量结构进行解释。* 为大量NERC资助的研究提供附加值。*这五个合作小组负责数十项不同研究中约2,000个微阵列的数据库。这是一个宝贵的资源，值得进一步分析。通常，只对数据进行第一遍分析。所有的研究都有其自身的复杂性，因此需要分析师和环境科学家之间的密切工作关系，以应用分析工具，这将是由PI和指定的研究人员的资助，这些NERC资助的研究和其中的大部分微阵列数据的大部分。

**A pipeline for arbitrarily designed experiments** Several NERC-funded projects have been making use of innovative, more efficient microarray designs. These designs are making use of the standard two-channel platform, but avoid using reference samples. The resulting designs can be up to 50% more efficient than traditional reference-based designs. The main difficulty of these designs is that commercial microarray analysis software is not geared up to analyse such experiments. An alternative, freely available research tool will be developed to deal with these as well as traditionally designed experiments. *Being able to deal with the complexities of sample availability.* Genomic experiments in general --- and especially within environmental settings --- are often designed in an ad hoc way that depends on the intersection of the availability of genomic material and the availability of the measuring platform. In practice this means that either technical replicates or biological samples are measured or biological samples are pooled into a single measurement sample. Ignoring these difficulties in further analysis --- which is standard practice today --- leads to biased and inaccurate results. The analysis tool, based on linear mixed-effect models, should give accurate results whatever the sample preparation, whether it involves pooling of samples or repeated measurement of the same sample. *Extending the concept of differential expression to arbitrarily complex treatment allocations.* The experiments considered by the NERC collaborators on this project are all much more complex than the initial comparisons of two treatments. For example, in the Paterson project (NE/D000602/1), 3 different stages of nematodes are sampled from the same host, which can be treated or untreated. In the Pottinger project (Small grant MGF107, Molecular Genetics Facility Steering Committee), the gene expression within the brains of stickleback fish are measured after giving males and females two different doses of oestrogen. A complicating issue is that it is unknown whether the phenotypically female fish treated with the high oestrogen dose are either females or sex-reversed males. In the Tyler projects (NER/T/S/2002/00182 and NE/C507696/1), males and females of two types of fish are exposed to different doses of two different environmental pollutants and are sampled across strategically selected developmental time points. In all of these examples, there is a complicated factorial treatment structure, which makes pairwise comparisons both unattractive and scientifically uninteresting. Another aspect of the new biology is the need to explore much more subtle phenotypes. In the Sneddon project (NER/I/S/2001/00768, NE/C000889/1, NE/C002164/1) the phenotypes that are compared on the microarray are subtly different --- e.g. bold trout and disheartened bold trout that have lost a dominance challenge. This falls outside of the capabilities of all but the specialist. The analysis tool should be able to deal with arbitrarily complex factorial covariate structures and produce meaningful results that can be interpreted in terms of those covariate structures. *Providing added value to a large number of NERC funded studies.* The five collaborative groups are between themselves responsible for a database of some 2,000 microarrays across tens of different studies. This is an invaluable resource, which deserves to be analyzed further. All too often, only a first pass analysis has been applied to the data. All of the studies have their own complexities and therefore a close working relationship between analysts and environmental scientists is required in order to apply the analysis tool, that will be developed by the PI and the named researcher on the grant, to a large fraction of these NERC funded studies and to the majority of the microarray data therein.