Linked Life Data

20529062

Source:http://linkedlifedata.com/resource/pubmed/id/20529062

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-6-10
pubmed:abstractText
The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1365-2818
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
238
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
145-61
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Image analysis benchmarking methods for high-content screen design.
pubmed:affiliation
Department of Biochemistry, Stanford Medical School, 279 Campus Drive, Beckman 409, Stanford, CA, USA.
pubmed:publicationType
Journal Article, Comparative Study