BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets.
BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets.
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DOI：
10.1186/1471-2105-11-73
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发表时间：
2010-02-03
影响因子：
3
通讯作者：
Lucchini S
中科院分区：
文献类型：
--
作者：
Alston MJ;Seers J;Hinton JC;Lucchini S
DNA微阵列的发展促进了成千上万个转录组数据集的产生。使用通用参考微阵列设计，可以根据新数据轻松比较和重新分析现有的转录组数据，并且将这种设计与大型数据集相结合，是“系统”级分析的理想选择。一个问题是，这些数据集通常是多年收集的，本质上可能是异构的，包含不同的微阵列文件格式和基因阵列布局，染料交换，并显示微阵列之间的log2-表达比的不同尺度。对于微阵列数据的规范化和分析存在优秀的软件，但许多数据尚未被分析，因为现有的方法与异构数据集斗争；选项包括在个体或实验组基础上规范化微阵列。我们的解决方案是在R （BABAR）算法和软件包中开发批处理反香蕉算法，该算法使用循环黄土对整个数据集进行归一化。我们已经使用BABAR分析了沙门氏菌基因在哺乳动物细胞感染过程中的功能。BABAR所需的唯一输入是未处理的GenePix或BlueFuse微阵列数据文件。BABAR提供了“内部”和“之间”微阵列标准化步骤和诊断箱线图的组合。当应用于真正的异构数据集时，BABAR对数据集进行规范化，以在微阵列之间产生可比的缩放，微阵列数据与RT-PCR分析非常一致。当应用于真实的非异构数据集和模拟数据集时，BABAR在识别差异表达基因方面的性能比标准技术显示出一些优势。BABAR是一个易于使用的软件工具，简化了异构双色通用参考设计cDNA微阵列转录组数据集的同时归一化。我们展示了BABAR转换真实和模拟数据集，以便对这些数据进行正确的解释，并且是促进识别差异表达基因或从转录组数据集进行网络推断分析的理想工具。
The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets.
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影响因子：
4.4
作者：
Grissom, SF;Lobenhofer, EK;Tucker, CJ
通讯作者：
Tucker, CJ
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：
5.8
作者：
Ballman, KV;Grill, DE;Therneau, TM
通讯作者：
Therneau, TM
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
5.8
作者：
Benito, M;Parker, J;Marron, JS
通讯作者：
Marron, JS