Recent advances in quantitative high throughput data analysis

Introduction High throughput screening has become a basic technique with which to explore biological systems. Advances in screening technology are driving the need for improvements in the analysis of the data sets derived from such screens. Areas covered The article will cover recent advances in the analysis of high throughput screening data sets from arrayed samples as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Expert opinion As data sets become larger and more complex the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. Article Highlights Analysis of Screening Data • Practical approaches to improving data quality • qHTS and the need for high throughput curve fitting • Analysis of combinations • Modeling approaches to help improve data quality Analysis of Image Based Screening Data sets • The use of image based assay methods for both target based and phenotypic based discovery projects • Advances in image analysis pipelines using cloud and cluster based computing • The analysis of multiparametric data sets derived from image based assays • Unbiased discovery of novel phenotypes with clustering methods Analysis of Cytometry Data Sets (FACS and CyTOF) • Advances in cytomterty with new detectors allows additional parameters to be monitored, generating new multiparametric data sets • Cytometry generates cell by cell data sets requiring new analysis methods Analysis of Genetic Screening Data Sets • Analysis of genetic screens introduces the added complexity of multiple reagents targeting each gene • Review of the analysis methods proposed to exploit the redundancy of gene targeting reagents to help account of different efficacy as well as off target effect