Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts

Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processi...

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Detalles Bibliográficos
Autores principales: Hahne, Florian, Arlt, Dorit, Sauermann, Mamatha, Majety, Meher, Poustka, Annemarie, Wiemann, Stefan, Huber, Wolfgang
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779598/
https://www.ncbi.nlm.nih.gov/pubmed/16916453
http://dx.doi.org/10.1186/gb-2006-7-8-r77
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author Hahne, Florian
Arlt, Dorit
Sauermann, Mamatha
Majety, Meher
Poustka, Annemarie
Wiemann, Stefan
Huber, Wolfgang
author_facet Hahne, Florian
Arlt, Dorit
Sauermann, Mamatha
Majety, Meher
Poustka, Annemarie
Wiemann, Stefan
Huber, Wolfgang
author_sort Hahne, Florian
collection PubMed
description Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems.
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spelling pubmed-17795982007-01-19 Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts Hahne, Florian Arlt, Dorit Sauermann, Mamatha Majety, Meher Poustka, Annemarie Wiemann, Stefan Huber, Wolfgang Genome Biol Method Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems. BioMed Central 2006 2006-08-17 /pmc/articles/PMC1779598/ /pubmed/16916453 http://dx.doi.org/10.1186/gb-2006-7-8-r77 Text en Copyright © 2006 Hahne et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Hahne, Florian
Arlt, Dorit
Sauermann, Mamatha
Majety, Meher
Poustka, Annemarie
Wiemann, Stefan
Huber, Wolfgang
Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title_full Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title_fullStr Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title_full_unstemmed Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title_short Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
title_sort statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779598/
https://www.ncbi.nlm.nih.gov/pubmed/16916453
http://dx.doi.org/10.1186/gb-2006-7-8-r77
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