Can three incongruence tests predict when data should be combined?
Can three incongruence tests predict when data should be combined?
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DOI：
10.1093/oxfordjournals.molbev.a025813
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发表时间：
1997-07-01
影响因子：
10.7
通讯作者：
Cunningham, CW
中科院分区：
文献类型：
--
作者：
Cunningham, CW
条件组合的倡导者认为，测试数据分区之间的不一致是数据探索的重要一步。除非分区具有不同的历史（如水平基因转移），否则不一致意味着一个或多个数据分区支持错误的系统发育。本研究使用三种不一致的统计检验来检验不一致和系统发育准确性之间的关系。这些测试应用于来自两个得到充分证实的脊椎动物系统发育的线粒体 DNA 数据分区对。在这三个测试中，最有用的是不一致长度差异测试（ILD，也称为分区同质性测试）。该测试区分了组合数据通常提高系统发育准确性的情况（P > 0.01）和组合数据的准确性相对于各个分区受到影响的情况（P < 0.001）。相反，在一些情况下，Templeton 和 Rodrigo 测试检测到高度显着的不一致 (P < 0.001)，即使组合不一致的分区实际上提高了系统发育的准确性。所有三个测试都确定了改进重建模型可以提高各个分区的系统发育准确性的情况。
Advocates of conditional combination have argued that testing for incongruence between data partitions is an important step in data exploration. Unless the partitions have had distinct histories, as in horizontal gene transfer, incongruence means that one or more data partitions support the wrong phylogeny. This study examines the relationship between incongruence and phylogenetic accuracy using three statistical tests of incongruence. These tests were applied to pairs of mitochondrial DNA data partitions from two well-corroborated vertebrate phylogenies. Of the three tests, the most useful was the incongruence length difference test (ILD, also called the partition homogeneity test). This test distinguished between cases in which combining the data generally improved phylogenetic accuracy (P > 0.01) and cases in which accuracy of the combined data suffered relative to the individual partitions (P < 0.001). In contrast, in several cases, the Templeton and Rodrigo tests detected highly significant incongruence (P < 0.001) even though combining the incongruent partitions actually increased phylogenetic accuracy. All three tests identified cases in which improving the reconstruction model could improve the phylogenetic accuracy of the individual partitions.