virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.
virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.
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DOI：
10.1186/1471-2105-14-75
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发表时间：
2013-03-02
影响因子：
3
通讯作者：
Alt R
中科院分区：
文献类型：
--
作者：
Heider A;Alt R
微阵列已成为解决各种生物学问题的常规工具。因此，随着时间的推移，几家制造商已经生产了不同类型和不同代的微阵列。同样，存放在公共数据库（如NCBI GEO或EBI ArrayExpress）中的原始数据的多样性也大幅增长。这导致数据库目前包含由不同物种，制造商和芯片代聚类的几十万个微阵列样本。虽然这些数据库的最初目标之一是使数据可供其他研究人员进行独立分析，并在适当的情况下与他们自己的数据整合，但目前的软件实现无法提供这一功能。只有那些在同一芯片平台上生成的数据集才能容易地组合在一起，即使在这里也有批量效应需要考虑。一个简单的方法来处理多种芯片类型和批量效应一直缺失。这里介绍的软件旨在以方便和用户友好的方式解决这两个问题。virtualArray软件包可以根据NCBI GEO或Bioconductor的当前注释，使用几乎任何芯片类型组合联合收割机原始数据集。在为原始数据建立一致的注释后，virtualArray可以直接采用七种实现方法之一来调整由于所使用的芯片类型之间的差异而导致的数据中的批量效应。这两个步骤都可以根据用户的偏好进行调整。当运行完成时，整个数据集作为传统的Bioconductor“ExpressionSet”对象呈现，该对象可用作其他Bioconductor包的输入。使用这个软件包，研究人员可以很容易地将他们自己的微阵列数据与来自公共存储库或其他来源的基于不同微阵列芯片类型的数据整合起来。使用默认方法，将稳健且最新的批次效应校正技术应用于数据。
Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously. This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature. Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing. The software presented here was designed to solve both of these problems in a convenient and user friendly way. The virtualArray software package can combine raw data sets using almost any chip types based on current annotations from NCBI GEO or Bioconductor. After establishing congruent annotations for the raw data, virtualArray can then directly employ one of seven implemented methods to adjust for batch effects in the data resulting from differences between the chip types used. Both steps can be tuned to the preferences of the user. When the run is finished, the whole dataset is presented as a conventional Bioconductor “ExpressionSet” object, which can be used as input to other Bioconductor packages. Using this software package, researchers can easily integrate their own microarray data with data from public repositories or other sources that are based on different microarray chip types. Using the default approach a robust and up-to-date batch effect correction technique is applied to the data.
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DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
3
作者：
Hwang KB;Kong SW;Greenberg SA;Park PJ
通讯作者：
Park PJ
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
3.7
作者：
Danova-Alt R;Heider A;Egger D;Cross M;Alt R
通讯作者：
Alt R
影响因子：
3.5
作者：
Cahan, Patrick;Rovegno, Felicia;McCaffrey, Timothy A.
通讯作者：
McCaffrey, Timothy A.