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

BMC Bioinformatics. 2011;12(1):485 DOI 10.1186/1471-2105-12-485

 

Journal Homepage

Journal Title: BMC Bioinformatics

ISSN: 1471-2105 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics | Science: Biology (General)

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Knapp Bettina
Rebhan Ilka
Kumar Anil
Matula Petr
Kiani Narsis A
Binder Marco
Erfle Holger
Rohr Karl
Eils Roland
Bartenschlager Ralf
Kaderali Lars

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 19 weeks

 

Abstract | Full Text

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>