Deng Yue, Altschuler Steven J, Wu Lani F
Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.
Bioinformatics. 2016 Jun 15;32(12):i44-i51. doi: 10.1093/bioinformatics/btw251.
Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a single phenotypic feature (e.g. abundance) of a biomarker. These approaches are insufficient for analyzing perturbation data that can contain information about multiple features (e.g. abundance, activity or localization) of each biomarker.
We propose a computational framework for inferring phenotypic crosstalk (PHOCOS) that is suitable for high-content microscopy or other modalities that capture multiple phenotypes per biomarker. PHOCOS uses a robust graph-learning paradigm to predict direct effects from potential indirect effects and identify errors owing to noise or missing links. The result is a multi-feature, sparse network that parsimoniously captures direct and strong interactions across phenotypic attributes of multiple biomarkers. We use simulated and biological data to demonstrate the ability of PHOCOS to recover multi-attribute crosstalk networks from cellular perturbation assays.
PHOCOS is available in open source at https://github.com/AltschulerWu-Lab/PHOCOS CONTACT: steven.altschuler@ucsf.edu or lani.wu@ucsf.edu.
对细胞对扰动的变化进行量化可为推断生物网络中分子成分之间的串扰提供一种强大的方法。现有的串扰推断方法基于生物标志物的单一表型特征（例如丰度）进行网络结构学习。这些方法不足以分析可能包含每个生物标志物的多个特征（例如丰度、活性或定位）信息的扰动数据。
我们提出了一种用于推断表型串扰（PHOCOS）的计算框架，该框架适用于高内涵显微镜或其他能捕获每个生物标志物多种表型的模式。PHOCOS使用一种强大的图学习范式来预测潜在间接效应中的直接效应，并识别由噪声或缺失链接导致的误差。结果是一个多特征的稀疏网络，它简约地捕获了多个生物标志物表型属性之间的直接和强相互作用。我们使用模拟数据和生物数据来证明PHOCOS从细胞扰动实验中恢复多属性串扰网络的能力。
PHOCOS以开源形式提供，网址为https://github.com/AltschulerWu-Lab/PHOCOS 联系方式：steven.altschuler@ucsf.edu或lani.wu@ucsf.edu 。
