Robinson Mark D, Speed Terence P
Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia.
BMC Bioinformatics. 2009 May 22;10:156. doi: 10.1186/1471-2105-10-156.
The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events.
We propose a strategy to score differential splicing by using the auxiliary information from fitting the statistical model, RMA (robust multichip analysis). RMA partitions the probe-level data into probe effects and expression levels, operating robustly so that if a small number of probes behave differently than the rest, they are downweighted in the fitting step. We argue that adjacent poorly fitting probes for a given sample can be evidence of differential splicing and have designed a statistic to search for this behaviour. Using a public tissue panel dataset, we show many examples of tissue-specific alternative splicing. Furthermore, we show that evidence for putative alternative splicing has a strong correspondence between the Gene 1.0 ST and Exon 1.0 ST platforms.
We propose a new approach, FIRMAGene, to search for differentially spliced genes using the Gene 1.0 ST platform. Such an analysis complements the search for differential expression. We validate the method by illustrating several known examples and we note some of the challenges in interpreting the probe-level data.Software implementing our methods is freely available as an R package.
最新一代的Affymetrix微阵列旨在检测每个基因座的全长表达情况，从而为全基因组范围内的可变剪接研究提供了机会。外显子1.0 ST（正义靶标）平台有针对人类、小鼠和大鼠的版本，主要用于探测每个已知或预测的外显子。较小的基因1.0 ST阵列被设计为表达微阵列，但仍使用沿着每个充分表征的转录本全长的探针来检测表达。我们探索了使用基因1.0 ST平台识别差异剪接事件的可能性。
我们提出了一种策略，通过使用拟合统计模型RMA（稳健多芯片分析）的辅助信息来对差异剪接进行评分。RMA将探针水平的数据划分为探针效应和表达水平，其操作稳健，因此如果少数探针的行为与其他探针不同，它们在拟合步骤中会被降权。我们认为，给定样本中相邻的拟合不佳的探针可能是差异剪接的证据，并设计了一个统计量来搜索这种行为。使用一个公共组织样本数据集，我们展示了许多组织特异性可变剪接的例子。此外，我们表明，假定的可变剪接证据在基因1.0 ST和外显子1.0 ST平台之间有很强的对应关系。
我们提出了一种新方法FIRMAGene，用于使用基因1.0 ST平台搜索差异剪接基因。这种分析补充了差异表达的搜索。我们通过举例说明几个已知的例子来验证该方法，并指出了解释探针水平数据时的一些挑战。实现我们方法的软件可作为R包免费获取。
