Researchers at Swiss life sciences software firm Genedata have developed a method to analyze multi-parameter data from high-content screening experiments that uses distance measurements and hierarchical clustering.
The researchers also developed a method to select hits based on machine-learning methods that they claim can increase the hit-verification rate five-fold over conventional methods based on single-readout assays.
Cluster analysis of the data matched “observed parameter correlations with biological context and mechanisms.”
“Our work was intended to demonstrate how modern statistical methods can be applied to optimally leverage the complex data sets resulting from HCS campaigns,” said Oliver Düerr, lead author of the paper and a scientific consultant at Genedata.
The researchers used a phenotypic neurite outgrowth screen to demonstrate their data-analysis platform because molecules producing neurite outgrowth have potential as neuroprotective agents, Stephan Heyse, general manager of the Screener business unit at Genedata, told CBA News this week.
He said that although current imaging technologies and image-analysis algorithms can enable researchers to rapidly and comprehensively screen many compounds, the multiple-parameter readouts provided by the high-content approach used by his company in the study require a sophisticated and efficient data-analysis environment.
The data generated by the neurite outgrowth assay was analyzed using the Genedata Screener, Mathworks Matlab, and libSVM software packages. The work appears in the December 2007 issue of the Journal of Biomolecular Screening.
“Our work was intended to demonstrate how modern statistical methods can be applied to optimally leverage the complex data sets resulting from HCS campaigns.”
For the current publication, Genedata collaborated with Merck Serono. According to Dominique Besson, an author on the paper and a group leader at Merck Serono, Merck did the necessary lab work using the Cellomics platform, and data analysis using the standard Screener software package. The statistical analysis and the machine learning were done by Genedata.
“Merck Serono has worked with Genedata since 2004, when it decided to integrate the Genedata Screener software to support primary and secondary screening data management,” Besson told CBA News in an e-mail this week.
He went on to say that Merck decided to use the Screener software after a short parallel evaluation of several marketed solutions, followed by a one-year full evaluation of Genedata Screener on-site.
The Screener software is “easy to use and features integrated statistics, blind data QC, pattern recognition, and pattern correction,” Besson said. It is easy to integrate with other corporate databases and processes, quickly yields robust data, and has very efficient communication tools, technical support, and response time.
Düerr said his company is working with different HCS instrumentation vendors to allow for the smooth transfer of data into Genedata Screener.
He said the program “allows data transfer from all instruments currently on the market,” and said Genedata has been working with Evotec Technologies “to configure data importation” for its Opera reader; with Cellomics; and with other vendors.
Genedata would also like to further refine the system so that it is better able to expedite laboratory workflow, Düerr said.
To this end, the company is collaborating with existing customers to solicit constructive feedback, said Heyse. He declined to elaborate.