Comparison and visualisation of agreement for paired lists of rankings.

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Margaret R Donald, Susan R Wilson
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引用次数: 0

Abstract

Output from analysis of a high-throughput 'omics' experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the k top-ranked items will be in agreement. So the aim is to find the point k at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for k. R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.

比较和可视化的协议配对列表的排名。
高通量“组学”实验的分析结果通常是一个排名列表。一个常见的例子是来自基因表达实验的差异表达基因的排序列表,长度为数百个基因。在许多情况下,我们感兴趣的是比较两个(或更多)不同实验之后的输出,或者比较产生不同排名列表的不同分析方法。如果两个排名表的排名相差一定的距离,我们就认为它们是一致的,而不是考虑它们之间的排名是否完全一致。一般来说,只有k个排名靠前的项目中相对较小的一部分是一致的。因此,我们的目标是找到k点,在这个点上,排名一致的概率从大于0.5变为小于0.5。我们使用惩罚样条和贝叶斯逻辑模型,给出了协议序列的非参数平滑,以及协议概率的点向可信区间。我们的方法产生k的点估计和可信区间。提供了R代码。该方法应用于乳腺癌微阵列实验中基因的排序。
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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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