Choi Hosik, Gim Jungsoo, Won Sungho, Kim You Jin, Kwon Sunghoon, Park Changyi
Department of Applied Statistics, Kyonggi University, Suwon, 16227, Korea.
Institute of Health and Environment, Seoul National University, Seoul, 08826, Korea.
BMC Genet. 2017 Nov 6;18(1):93. doi: 10.1186/s12863-017-0561-z.
Undirected graphical models or Markov random fields have been a popular class of models for representing conditional dependence relationships between nodes. In particular, Markov networks help us to understand complex interactions between genes in biological processes of a cell. Local Poisson models seem to be promising in modeling positive as well as negative dependencies for count data. Furthermore, when zero counts are more frequent than are expected, excess zeros should be considered in the model.
We present a penalized Poisson graphical model for zero inflated count data and derive an expectation-maximization (EM) algorithm built on coordinate descent. Our method is shown to be effective through simulated and real data analysis.
Results from the simulated data indicate that our method outperforms the local Poisson graphical model in the presence of excess zeros. In an application to a RNA sequencing data, we also investigate the gender effect by comparing the estimated networks according to different genders. Our method may help us in identifying biological pathways linked to sex hormone regulation and thus understanding underlying mechanisms of the gender differences.
We have presented a penalized version of zero inflated spatial Poisson regression and derive an efficient EM algorithm built on coordinate descent. We discuss possible improvements of our method as well as potential research directions associated with our findings from the RNA sequencing data.
无向图模型或马尔可夫随机场一直是用于表示节点间条件依赖关系的一类流行模型。特别是，马尔可夫网络有助于我们理解细胞生物过程中基因之间的复杂相互作用。局部泊松模型在对计数数据的正相关和负相关进行建模方面似乎很有前景。此外，当零计数比预期更频繁时，模型中应考虑过多的零值。
我们提出了一种针对零膨胀计数数据的惩罚泊松图模型，并推导了基于坐标下降的期望最大化（EM）算法。通过模拟和实际数据分析表明我们的方法是有效的。
模拟数据的结果表明，在存在过多零值的情况下，我们的方法优于局部泊松图模型。在对RNA测序数据的应用中，我们还通过比较不同性别的估计网络来研究性别效应。我们的方法可能有助于我们识别与性激素调节相关的生物途径，从而理解性别差异的潜在机制。
我们提出了零膨胀空间泊松回归的惩罚版本，并推导了基于坐标下降的高效EM算法。我们讨论了我们方法可能的改进以及与RNA测序数据结果相关的潜在研究方向。
