KNAPP, Bettina, Ilka REBHAN, Anil KUMAR, Petr MATULA, Narsis A KIANI, Marco BINDER, Hoger ERFLE, Karl ROHR, Roland EILS, Ralf BARTENSCHLAGER and Lars KADERALI. Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens. BMC Bioinformatics, BioMed Central, 2011, vol. 12, No 485, p. 1-14. ISSN 1471-2105.
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Basic information
Original name Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens
Authors KNAPP, Bettina (276 Germany), Ilka REBHAN (276 Germany), Anil KUMAR (356 India), Petr MATULA (203 Czech Republic, guarantor, belonging to the institution), Narsis A KIANI (364 Islamic Republic of Iran), Marco BINDER (276 Germany), Hoger ERFLE (276 Germany), Karl ROHR (276 Germany), Roland EILS (276 Germany), Ralf BARTENSCHLAGER (276 Germany) and Lars KADERALI (276 Germany).
Edition BMC Bioinformatics, BioMed Central, 2011, 1471-2105.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10103 Statistics and probability
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 2.751
RIV identification code RIV/00216224:14330/11:00054378
Organization unit Faculty of Informatics
UT WoS 000299110500001
Keywords in English high-content screening; normalization; cell-based analysis
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Petr Matula, Ph.D., učo 3019. Changed: 17/4/2012 14:20.
Abstract
We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell’s individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a nonvirus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.
Links
MSM0021622419, plan (intention)Name: Vysoce paralelní a distribuované výpočetní systémy
Investor: Ministry of Education, Youth and Sports of the CR, Research Intents
2B06052, research and development projectName: Vytipování markerů, screening a časná diagnostika nádorových onemocnění pomocí vysoce automatizovaného zpracování multidimenzionálních biomedicínských obrazů (Acronym: Biomarker)
Investor: Ministry of Education, Youth and Sports of the CR, Health and quality of life
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