什么时候Menzerath-Altmann定律在数学上是微不足道的?一种新的方法。

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ramon Ferrer-i-Cancho, Antoni Hernández-Fernández, Jaume Baixeries, Łukasz Dębowski, Ján Mačutek
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引用次数: 22

摘要

门泽拉斯定律,即Z(部分的平均大小)随着X(部分的数量)的增加而减小的趋势,在语言、音乐和基因组中都有发现。最近,有人认为该定律在基因组中的存在是Z=Y/X这一事实的必然结果,这意味着Z与X的比例为Z ~ 1/X。这种缩放是Menzerath-Altmann定律的一个非常特殊的例子,它被基因组中X和Y之间的相关性测试所拒绝,X是一个物种的染色体数量,Y是它的基因组碱基大小,Z是平均染色体大小。在这里,我们回顾了该测试的统计基础,并考虑了基于不同相关指标的三个非参数测试和一个参数测试来评估Z ~ 1/X是否在基因组中。最强大的检验是基于相关比率的新型非参数检验,它能够在11个分类类群中排除9个Z ~ 1/X,并检测出一个边缘类群。Z ~ 1/X不是事实,而是真实基因组不符合的基线。Menzerath-Altmann法则不可避免的观点是有严重缺陷的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
When is Menzerath-Altmann law mathematically trivial? A new approach.

Menzerath's law, the tendency of Z (the mean size of the parts) to decrease as X (the number of parts) increases, is found in language, music and genomes. Recently, it has been argued that the presence of the law in genomes is an inevitable consequence of the fact that Z=Y/X, which would imply that Z scales with X as Z ∼ 1/X. That scaling is a very particular case of Menzerath-Altmann law that has been rejected by means of a correlation test between X and Y in genomes, being X the number of chromosomes of a species, Y its genome size in bases and Z the mean chromosome size. Here we review the statistical foundations of that test and consider three non-parametric tests based upon different correlation metrics and one parametric test to evaluate if Z ∼ 1/X in genomes. The most powerful test is a new non-parametric one based upon the correlation ratio, which is able to reject Z ∼ 1/X in nine out of 11 taxonomic groups and detect a borderline group. Rather than a fact, Z ∼ 1/X is a baseline that real genomes do not meet. The view of Menzerath-Altmann law as inevitable is seriously flawed.

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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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