Online phenotype discovery in high-content RNAi screens using gap statistics

Zheng Yin, Xiaobo Zhou, Chris Bakal, Fuhai Li, Youxian Sun, Norbert Perrimon, Stephen T C Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Discovering and identifying novel phenotypes from images inputting online is a major challenge in high-content RNA interference (RNAi) screens. Discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using gap statistics is proposed. The method works well on discovering new phenotypes adaptively when applied to both of synthetic data sets and RNAi high content screen (HCS) images with ground truth labels.

Original languageEnglish (US)
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages86-95
Number of pages10
Volume952
DOIs
StatePublished - Dec 1 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: Dec 17 2007Dec 19 2007

Other

Other2007 International Symposium on Computational Models for Life Sciences, CMLS '07
CountryAustralia
CityGold Coast, QLD
Period12/17/0712/19/07

Keywords

  • Gap statistics
  • High content screen
  • Phenotype discovery
  • RNA interference

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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