Literature DB >> 19548208

Automation in high-content flow cytometry screening.

U Naumann1, M P Wand.   

Abstract

High-content flow cytometric screening (FC-HCS) is a 21st Century technology that combines robotic fluid handling, flow cytometric instrumentation, and bioinformatics software, so that relatively large numbers of flow cytometric samples can be processed and analysed in a short period of time. We revisit a recent application of FC-HCS to the problem of cellular signature definition for acute graft-versus-host-disease. Our focus is on automation of the data processing steps using recent advances in statistical methodology. We demonstrate that effective results, on par with those obtained via manual processing, can be achieved using our automatic techniques. Such automation of FC-HCS has the potential to drastically improve diagnosis and biomarker identification.

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Year:  2009        PMID: 19548208     DOI: 10.1002/cyto.a.20754

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  8 in total

1.  Rapid cell population identification in flow cytometry data.

Authors:  Nima Aghaeepour; Radina Nikolic; Holger H Hoos; Ryan R Brinkman
Journal:  Cytometry A       Date:  2011-01       Impact factor: 4.355

Review 2.  Data analysis in flow cytometry: the future just started.

Authors:  Enrico Lugli; Mario Roederer; Andrea Cossarizza
Journal:  Cytometry A       Date:  2010-07       Impact factor: 4.355

3.  Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Authors:  Habil Zare; Parisa Shooshtari; Arvind Gupta; Ryan R Brinkman
Journal:  BMC Bioinformatics       Date:  2010-07-28       Impact factor: 3.169

4.  Soluble guanylate cyclase modulates alveolarization in the newborn lung.

Authors:  Patricia R Bachiller; Katherine H Cornog; Rina Kato; Emmanuel S Buys; Jesse D Roberts
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2013-08-09       Impact factor: 5.464

5.  Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Authors:  Greg Finak; Juan-Manuel Perez; Andrew Weng; Raphael Gottardo
Journal:  BMC Bioinformatics       Date:  2010-11-04       Impact factor: 3.169

6.  The curvHDR method for gating flow cytometry samples.

Authors:  Ulrike Naumann; George Luta; Matthew P Wand
Journal:  BMC Bioinformatics       Date:  2010-01-22       Impact factor: 3.169

7.  Merging mixture components for cell population identification in flow cytometry.

Authors:  Greg Finak; Ali Bashashati; Ryan Brinkman; Raphaël Gottardo
Journal:  Adv Bioinformatics       Date:  2009-11-12

8.  Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection.

Authors:  Maziyar Baran Pouyan; Vasu Jindal; Javad Birjandtalab; Mehrdad Nourani
Journal:  BMC Med Genomics       Date:  2016-08-10       Impact factor: 3.063

  8 in total

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