Non-parametric population analysis of cellular phenotypes. Med Image Comput Comput Assist Interv 2011;14(Pt 2):343-51
Date
10/15/2011Pubmed ID
21995047Pubmed Central ID
PMC4295822DOI
10.1007/978-3-642-23629-7_42Scopus ID
2-s2.0-82255164880 (requires institutional sign-in at Scopus site) 9 CitationsAbstract
Methods to quantify cellular-level phenotypic differences between genetic groups are a key tool in genomics research. In disease processes such as cancer, phenotypic changes at the cellular level frequently manifest in the modification of cell population profiles. These changes are hard to detect due the ambiguity in identifying distinct cell phenotypes within a population. We present a methodology which enables the detection of such changes by generating a phenotypic signature of cell populations in a data-derived feature-space. Further, this signature is used to estimate a model for the redistribution of phenotypes that was induced by the genetic change. Results are presented on an experiment involving deletion of a tumor-suppressor gene dominant in breast cancer, where the methodology is used to detect changes in nuclear morphology between control and knockout groups.
Author List
Singh S, Janoos F, Pécot T, Caserta E, Huang K, Rittscher J, Leone G, Machiraju RAuthor
Gustavo Leone PhD Senior Associate Dean, Center Director, Professor in the Pathology and Laboratory Medicine department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsAnimals
Breast Neoplasms
Cell Biology
Cell Nucleus
Cytological Techniques
Female
Fibroblasts
Humans
Image Processing, Computer-Assisted
Mice
Microscopy
Models, Theoretical
PTEN Phosphohydrolase
Phenotype
