M Yu Kevin, J Cowen Lenore
Department of Computer Science, Tufts University, Medford, MA, 02155, USA.
Sci Rep. 2025 Aug 18;15(1):30169. doi: 10.1038/s41598-025-16353-2.
In S. cerevisiae, a large and rich collection of epistasis data has been collected. When this data comes from double knockouts, it has a natural representation as a signed and weighted graph, where the weight on an edge is computed based on deviation from the expected sickness or health of the double-deletion mutant as compared to its constituent single deletion mutants. Different probabilistic null models (minimum, multiplicative, and logarithmic) to set edge weights appropriately were studied empirically by Mani et al. where the goal was to determine the best weighting scheme for detecting the presence or absence of epistasic effect in an individual double knockout in isolation. On the other hand, approaches such as the LocalCut algorithm of Leiserson et al. look at the entire network, and search for graph-theoretic structure indicative of compensatory pathways. The effect of different edge weighting schemes on the biological pathways returned by algorithms such as LocalCut has not been previously studied. We compare the generalized Between Pathway Models produced by LocalCut under multiple different ways of calculating edge weights, and analyze the resulting collections of putative redundant pathways that are produced. We recover some known pathways, find some interesting new pathways as well as give broad recommendations for how to set the parameters of LocalCut to produce the most biologically relevant gene sets.
在酿酒酵母中，已经收集了大量丰富的上位性数据。当这些数据来自双基因敲除时，它可以自然地表示为一个带符号和权重的图，其中边的权重是根据双缺失突变体与其组成的单缺失突变体相比预期的病态或健康状况的偏差来计算的。Mani等人通过实证研究了不同的概率空模型（最小、乘法和对数模型），以适当设置边的权重，目的是确定在单独的个体双基因敲除中检测上位性效应存在与否的最佳加权方案。另一方面，诸如Leiserson等人的LocalCut算法等方法着眼于整个网络，并搜索指示补偿途径的图论结构。此前尚未研究过不同的边加权方案对LocalCut等算法返回的生物途径的影响。我们比较了LocalCut在多种不同计算边权重方式下产生的广义通路间模型，并分析了由此产生的假定冗余通路集合。我们恢复了一些已知通路，发现了一些有趣的新通路，并就如何设置LocalCut的参数以产生最具生物学相关性的基因集给出了广泛的建议。
