Semiparametric spectral modeling of the Drosophila connectome
Semiparametric spectral modeling of the Drosophila connectome
复制标题
果蝇连接体的半参数光谱建模
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Albert Cardona
中科院分区：
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作者：
C. Priebe;Youngser Park;M. Tang;A. Athreya;V. Lyzinski;J. Vogelstein;Yichen Qin;Benjamin T. Cocanougher;K. Eichler;Marta Zlatic;Albert Cardona
我们提出了完整的幼虫果蝇蘑菇体连接体的半参数光谱建模。出于一个彻底的探索性数据分析的网络通过高斯混合建模（GMM）在邻接谱嵌入（ASE）表示空间，我们引入了潜在的结构模型（LSM）的网络建模和推理。LSM是随机块模型（SBM）的推广和随机点积图（RDPG）潜在位置模型的特殊情况下，并服从ASE表示空间中的半参数GMM。由此产生的连接体代码来自通过半参数GMM组成的ASE捕获潜在的连接体结构，并阐明生物相关的神经元特性。
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.