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Publication details

Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens

Basic information
Original title:	Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens
Authors:	Bettina Knapp, Ilka Rebhan, Anil Kumar, Petr Matula, Narsis A Kiani, Marco Binder, Hoger Erfle, Karl Rohr, Roland Eils, Ralf Bartenschlager, Lars Kaderali

Further information
Citation:	KNAPP, Bettina - REBHAN, Ilka - KUMAR, Anil - MATULA, Petr - KIANI, Narsis A - BINDER, Marco - ERFLE, Hoger - ROHR, Karl - EILS, Roland - BARTENSCHLAGER, Ralf - KADERALI, Lars. Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens. BMC Bioinformatics, BioMed Central, Great Britain. ISSN 1471-2105, 2011, vol. 12, no. 485, pp. 1-14.
Original language:	English
Field:	Applied statistics, operation research
WWW:	http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf
Type:	Article in Periodical
Keywords:	high-content screening; normalization; cell-based analysis

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.

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