HCS-PhenoCluster-Machine Learning based High-Content Phenotypic Analysis Tool
Computational analysis of multi-parametric high-content imaging dataset can be particularly tedious and daunting to implement for a laboratory biologist with no bioinformatics expertise and hence,there is an unmet need of software tools to facilitate this type of analysis.We present a web-based application HCS-PhenoCluster for the analysis of high-content image-based data which can be used for discovering novel cellular phenotypes beyond visual inspection.This application is interfaced with a single-cell extraction pipeline which performs cell segmentation and multi-feature extraction of high-content imaging data.HCS-PhenoCluster has been implemented in Swift language using Vapor framework and consists of a MySQL database.The image analysis workflow of HCS-PhenoCluster is based on machine learning models implemented in Python and comprises five modules of data processing which include multi-level quality control modules and an unsupervised clustering module to reveal phenotypic diversity in the dataset.We present a case study of the application of this tool for detecting multiple Golgi organizational states.The user manual including the description of methodology and relevant references are available with the application.
High-content screening Machine learning Phenotype discovery Unsupervised clustering Web-based software
Shaista Hussain Tan May Ling Xavier Le Guezennec Frédéric Bard
Institute of High Performance Computing Singapore 138632 Institute of Molecular and Cell Biology Singapore 138673
国际会议
成都
英文
157-161
2018-03-12（万方平台首次上网日期，不代表论文的发表时间）