J Biomed 2017; 2:78-88. doi:10.7150/jbm.17341

Review

Multiparametric Analysis of High Content Screening Data

Karol Kozak1,2✉, Julia Seeliger1, Tomasz Gedrange1

1. Clinic for Neurology, Carl Gustav Carus Campus, Technische Universität Dresden, Fetscherstr. 74, D-01307 Dresden, Germany;
2. Department of Informatics, Wroclaw University of Economics, Komandorska 118-120, 53-345 Wrocław, Poland;
3. Department of Orthodontics, Carl Gustav Carus Campus, Technische Universität Dresden, Fetscherstr. 74, D-01307 Dresden, Germany.

Abstract

Cell-based High-Content Screening (HCS) using automated microscopy is an upcoming methodology for the investigation of cellular processes and their alteration by multiple chemical or genetic perturbations. The analysis of the large amount of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. This article reviews the different ways to analyse large sets of HCS data, including the questions that can be asked and the challenges in interpreting the measurements. The main data mining approaches used in HCS, such as image descriptors computations and classification algorithms, are outlined.

Keywords: High-Content Screening, cellular processes, data mining

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
How to cite this article:
Kozak K, Seeliger J, Gedrange T. Multiparametric Analysis of High Content Screening Data. J Biomed 2017; 2:78-88. doi:10.7150/jbm.17341. Available from http://www.jbiomed.com/v02p0078.htm