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Open Access Research article

Normalizing for individual cell population context in the analysis of high-content cellular screens

Bettina Knapp16, Ilka Rebhan2, Anil Kumar2, Petr Matula34, Narsis A Kiani16, Marco Binder2, Holger Erfle5, Karl Rohr3, Roland Eils3, Ralf Bartenschlager2 and Lars Kaderali16*

Author Affiliations

1 Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany

2 Heidelberg University, Department of Infectious Diseases, Molecular Virology, Im Neuenheimer Feld 345, 69120 Heidelberg, Germany

3 Heidelberg University, Integrative Bioinformatics and Systems Biology, BioQuant, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany

4 Center for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic

5 Heidelberg University, BioQuant/Cellnetworks RNAi Screening Facility, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany

6 University of Technology Dresden, Medical Faculty, Institute for Medical Informatics and Biometry, Fetscherstrasse 74, 01307 Dresden, Germany

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BMC Bioinformatics 2011, 12:485  doi:10.1186/1471-2105-12-485

Published: 20 December 2011

Abstract

Background

High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology.

Results

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 non-virus 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.

Conclusions

Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.