Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2011 Dec 20;12:485. doi: 10.1186/1471-2105-12-485.

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

Author information

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

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.

PMID:
22185194
[PubMed - indexed for MEDLINE]
PMCID:
PMC3259109
Free PMC Article

Images from this publication.See all images (4)Free text

Figure 1
Figure 2
Figure 3
Figure 4
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk