Publication details

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

Authors

KNAPP Bettina REBHAN Ilka KUMAR Anil MATULA Petr KIANI Narsis A BINDER Marco ERFLE Hoger ROHR Karl EILS Roland BARTENSCHLAGER Ralf KADERALI Lars

Year of publication 2011
Type Article in Periodical
Magazine / Source BMC Bioinformatics
MU Faculty or unit

Faculty of Informatics

Citation
Web http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf
Field Applied statistics, operation research
Keywords high-content screening; normalization; cell-based analysis
Description 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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