Reducing the algorithmic variability in transcriptome-based inference
Reducing the algorithmic variability in transcriptome-based inference
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DOI：
10.1093/bioinformatics/btq104
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发表时间：
2010-05-01
期刊：
影响因子：
5.8
通讯作者：
Niranjan, Mahesan
中科院分区：
文献类型：
--
作者：
Tuna, Salih;Niranjan, Mahesan
动机：从微阵列中高通量测量信使核糖核酸丰度涉及几个预处理阶段。在每个阶段，用户都可以使用大量的算法，但对于使用哪种算法没有达成一致的指导意见。结果：转录组数据的二进制表示具有减少由于算法选择而在预处理阶段引入的变异性的理想特性。我们比较了算法选择在不同问题上的效果，并建议将微阵列数据的二进制表示与TAnimoto核用于支持向量机，减少了算法选择的影响，同时提高了表型分类的性能。
Motivation: High-throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies.Results: Binary representation of transcriptome data has the desirable property of reducing the variability introduced at the preprocessing stages due to algorithmic choice. We compare the effect of the choice of algorithms on different problems and suggest that using binary representation of microarray data with Tanimoto kernel for support vector machine reduces the effect of the choice of algorithm and simultaneously improves the performance of classification of phenotypes.