Literature DB >> 24598104

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond.

Yann Abraham1, Xian Zhang2, Christian N Parker2.   

Abstract

Advances in instrumentation now allow the development of screening assays that are capable of monitoring multiple readouts such as transcript or protein levels, or even multiple parameters derived from images. Such advances in assay technologies highlight the complex nature of biology and disease. Harnessing this complexity requires integration of all the different parameters that can be measured rather than just monitoring a single dimension as is commonly used. Although some of the methods used to combine multiple measurements, such as principal component analysis, are commonly used for microarray analysis, biologists are not yet using many of the tools that have been developed in other fields to address such issues. Visualization of multiparametric data sets is one of the major challenges in this field, and a depiction of the results in a manner that can be readily interpreted is essential. This article describes a number of assay systems being used to generate such data sets en masse, and the methods being applied to their visualization and analysis. We also discuss some of the challenges of applying methods developed in other fields to biology.
© 2014 Society for Laboratory Automation and Screening.

Keywords:  cell-based screening; high-content screening; machine learning; multiparametric data analysis; multiparametric visualization

Mesh:

Substances:

Year:  2014        PMID: 24598104     DOI: 10.1177/1087057114524987

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  13 in total

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2.  Recent Advances in Design of Fluorescence-Based Assays for High-Throughput Screening.

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Review 3.  High content analysis in amyotrophic lateral sclerosis.

Authors:  Federica Rinaldi; Dario Motti; Laura Ferraiuolo; Brian K Kaspar
Journal:  Mol Cell Neurosci       Date:  2016-12-11       Impact factor: 4.314

4.  Supervised discriminant analysis for droplet micro-magnetofluidics.

Authors:  Gungun Lin; Vladimir M Fomin; Denys Makarov; Oliver G Schmidt
Journal:  Microfluid Nanofluidics       Date:  2015-04-10       Impact factor: 2.529

5.  Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream targets and downstream phenotypes.

Authors:  Steffen Renner; Christian Bergsdorf; Rochdi Bouhelal; Magdalena Koziczak-Holbro; Andrea Marco Amati; Valerie Techer-Etienne; Ludivine Flotte; Nicole Reymann; Karen Kapur; Sebastian Hoersch; Edward James Oakeley; Ansgar Schuffenhauer; Hanspeter Gubler; Eugen Lounkine; Pierre Farmer
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Review 6.  Patient Registries in Idiopathic Pulmonary Fibrosis.

Authors:  Daniel A Culver; Jürgen Behr; John A Belperio; Tamera J Corte; Joao A de Andrade; Kevin R Flaherty; Mridu Gulati; Tristan J Huie; Lisa H Lancaster; Jesse Roman; Christopher J Ryerson; Hyun J Kim
Journal:  Am J Respir Crit Care Med       Date:  2019-07-15       Impact factor: 21.405

Review 7.  In vitro Assays and Imaging Methods for Drug Discovery for Cardiac Fibrosis.

Authors:  Giorgia Palano; Ariana Foinquinos; Erik Müllers
Journal:  Front Physiol       Date:  2021-07-08       Impact factor: 4.566

8.  Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

Authors:  Albert H Gough; Ning Chen; Tong Ying Shun; Timothy R Lezon; Robert C Boltz; Celeste E Reese; Jacob Wagner; Lawrence A Vernetti; Jennifer R Grandis; Adrian V Lee; Andrew M Stern; Mark E Schurdak; D Lansing Taylor
Journal:  PLoS One       Date:  2014-07-18       Impact factor: 3.240

9.  Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

Authors:  Nathaniel R Twarog; Jonathan A Low; Duane G Currier; Greg Miller; Taosheng Chen; Anang A Shelat
Journal:  PLoS One       Date:  2016-02-17       Impact factor: 3.240

10.  Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology.

Authors:  Ali Rohani; Jennifer A Kashatus; Dane T Sessions; Salma Sharmin; David F Kashatus
Journal:  Sci Rep       Date:  2020-11-03       Impact factor: 4.379

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