PHOCOS: inferring multi-feature phenotypic crosstalk networks.
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.
Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.
Yue
Y
Deng
Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.
Steven J
SJ
Altschuler
Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.
Lani F
LF
Wu
D015415:Biomarkers; D006801:Humans; D000069550:Machine Learning; D008853:Microscopy; D009504:Neutrophils / Q000166:cytology; D010641:Phenotype; D020239:Receptor Cross-Talk; D015398:Signal Transduction
D015415:Biomarkers