aRrayLasso: a network-based approach to microarray interconversion.
aRrayLasso: a network-based approach to microarray interconversion.
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DOI：
10.1093/bioinformatics/btv469
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发表时间：
2015-12-01
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影响因子：
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通讯作者：
Patel CJ
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文献类型：
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作者：
Brown AS;Patel CJ
总结：需要在微阵列平台之间进行稳健的转换，以利用迄今为止已经进行的各种各样的微阵列表达研究。目前可用的转换方法依赖于制造商注释，这通常是不完整的，或者依赖于来自不同平台的探针的直接比对，这通常不能产生可接受的基因相关性。在这里，我们描述了一个RrayLasso，它使用Lasso惩罚广义线性模型来模拟不同探针集中的各个探针之间的关系。我们已经在一组五个开源R函数中实现了aRrayLasso，允许用户从公共来源（如Gene Expression Omnibus）获取数据，在该数据上训练一组Lasso模型，并直接将一个微阵列平台映射到另一个微阵列平台。aRrayLasso显著预测表达水平，与相同RNA池的技术重复具有相似的保真度，证明了其在整合来自不同平台的数据集方面的实用性。可用性和实现：所有函数都可以在https://github.com/adam-sam-brown/aRrayLasso上找到，沿着说明。联系方式：chirag_patel@hms.harvard.edu补充信息：补充数据可从生物信息学在线网站获得。
Summary: Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describe aRrayLasso, which uses the Lasso-penalized generalized linear model to model the relationships between individual probes in different probe sets. We have implemented aRrayLasso in a set of five open-source R functions that allow the user to acquire data from public sources such as Gene Expression Omnibus, train a set of Lasso models on that data and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with similar fidelity to technical replicates of the same RNA pool, demonstrating its utility in the integration of datasets from different platforms. Availability and implementation: All functions are available, along with descriptions, at https://github.com/adam-sam-brown/aRrayLasso. Contact: chirag_patel@hms.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.