Format

Send to

Choose Destination
Bioinformatics. 2015 Mar 15;31(6):940-7. doi: 10.1093/bioinformatics/btu759. Epub 2014 Nov 14.

Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images.

Author information

1
Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada.
2
Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada.

Abstract

MOTIVATION:

Quantifying variability in protein expression is a major goal of systems biology and cell-to-cell variability in subcellular localization pattern has not been systematically quantified.

RESULTS:

We define a local measure to quantify cell-to-cell variability in high-throughput microscope images and show that it allows comparable measures of variability for proteins with diverse subcellular localizations. We systematically estimate cell-to-cell variability in the yeast GFP collection and identify examples of proteins that show cell-to-cell variability in their subcellular localization.

CONCLUSIONS:

Automated image analysis methods can be used to quantify cell-to-cell variability in microscope images.

PMID:
25398614
PMCID:
PMC4380034
DOI:
10.1093/bioinformatics/btu759
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center