title: Normalizing for individual cell population context in the analysis of high-content cellular screens.
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.
authors.affiliation: Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
authors.forename: Bettina
authors.initials: B
authors.lastname: Knapp
authors.forename: Ilka
authors.initials: I
authors.lastname: Rebhan
authors.forename: Anil
authors.initials: A
authors.lastname: Kumar
authors.forename: Petr
authors.initials: P
authors.lastname: Matula
authors.forename: Narsis A
authors.initials: NA
authors.lastname: Kiani
authors.forename: Marco
authors.initials: M
authors.lastname: Binder
authors.forename: Holger
authors.initials: H
authors.lastname: Erfle
authors.forename: Karl
authors.initials: K
authors.lastname: Rohr
authors.forename: Roland
authors.initials: R
authors.lastname: Eils
authors.forename: Ralf
authors.initials: R
authors.lastname: Bartenschlager
authors.forename: Lars
authors.initials: L
authors.lastname: Kaderali
mesh_terms: D002460:Cell Line; D048429:Cell Size; D003715:Dengue / Q000235:genetics* / Q000378:metabolism / Q000473:pathology* / Q000821:virology; D003716:Dengue Virus; D055785:Gene Knockdown Techniques; D016174:Hepacivirus; D006526:Hepatitis C / Q000235:genetics* / Q000378:metabolism / Q000473:pathology* / Q000821:virology; D006801:Humans; D010770:Phosphotransferases / Q000235:genetics*; D034622:RNA Interference; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059010:Single-Cell Analysis / Q000379:methods*
chemical_list: D010770:Phosphotransferases