Median networks

In the median-network approach, sequences   are first converted to binary data and constant sites are eliminated. Each   split is encoded as a binary character with states 0 and 1. Sites that   support the same split are grouped in one character, which is weighted by the   number of sites grouped. This leads to the representation of haplotypes as   0–1 vectors. Median or consensus vectors are calculated for each triplet of   vectors until the median network is finished. For >30 haplotypes, the   resulting median networks are impractical to display, owing to the presence   of high-dimensional hypercubes. Fortunately, the network can be reduced (i.e.   some loops can be solved) using predictions from coalescent theory (Box 2).   All the most parsimonious trees are guaranteed to be represented in a median network.   Although mainly aimed at mtDNA data, median networks can be estimated from   other kinds of data, as long as the data are binary or can be reduced to   binary data.

中值网络

在中值网络方法中，先将序列转换为二进制数据，然后消除保守位点。每个拆分都被编码为具有状态0和1的二进制字符。支持相同拆分的站点被分组为一个特征，该特征由分组的位点数加权。这导致单倍型的表现为0-1向量。对每三组向量计算中值向量或一致向量，直到中值网络完成。对于大于30个单倍型，由于存在高维超立方体，因此产生的中值网络无法显示。幸运的是，可以使用溯祖理论（框注2）的预测来减少网络（即可以解决一些循环）。所有最简约的树都可以用中值网络表示。尽管中值网络主要针对线粒体DNA数据，但只要数据是二进制的或可以简化为二进制数据，就可以从其他类型的数据中估计出中值网络。