Informace o publikaci

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

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

Rok publikování 2011
Druh Článek v odborném periodiku
Časopis / Zdroj BMC Bioinformatics
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf
Obor Aplikovaná statistika, operační výzkum
Klíčová slova high-content screening; normalization; cell-based analysis
Popis 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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