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MegaClust: a service allowing companies to outsource the analysis of their flow cytometry datasets.
Developed and operated by experts
MegaClust is developed and operated by the Vital-IT Bioinformatics Competence Center. Vital-IT maintains the high-performance computing (HPC) and storage infrastructure of the Swiss Institute of Bioinfomatics (SIB).
Integrated analysis platform
MegaClust consists of:
- A software implementation of the MegaClust analysis algorithm that is optimized for massive parallelization.
- An IT infrastructure consisting of 1’500 processors.
- Multidisciplinary expertise required for optimal data analysis (ranging from software engineers to biologists).
Flexible analysis workflows
The MegaClust analysis workflow is designed to be easily and quickly adaptable to the specificities inherent to each dataset.
Works on “anonymised” data
MegaClust is data agnostic. Sensitive information regarding individuals and markers are not needed and can therefore be removed from the data submitted to MegaClust.
Multiple reporting formats
MegaClust provides multiple output formats ranging from PDF summary reports to detailed Excel sheets.
MegaClust is a service allowing companies to analyze their flow cytometry datasets
MegaClust performs an exhaustive and unbiased identification of cell groups in flow cytometry datasets consisting of multiple samples. It provides a powerful way to:
- Stratify individuals in a cohort and provide insight on the stratification at the functional level.
- Analyze variations in cell subpopulations upon combined stimulations and candidate treatments, thus providing an insight on their mechanism of action and pharmacodynamics.
Download documents
Application note
Automated interpretation of a 1.5 M cells flow cytometry dataset resulting in the successful stratification of 50 individuals
Automated interpretation of a 1.5 M cells flow cytometry dataset resulting in the successful stratification of 50 individuals
This application note describes the use of MegaClust for the identification of cell subpopulations in a flow cytometry dataset of 1.5 million events obtained by pooling acquisition data for 50 individual samples. It then shows how identified cell subpopulations enable stratification.
Application note
Automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action
Automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action
This application note describes the analysis with MegaClust of a CyTof dataset of 2.25 million cells. It illustrates how the unique approach used by MegaClust to identify cell populations across samples provides a very powerful tool for analyses of drugs mechanism of action.
Quartz Bio Qualifies MegaClust for Clinical Flow Cytometry Data Analysis
Quartz Bio SA announced today very positive results for MegaClust, the platform developed jointly with the SIB Swiss Institute of Bioinformatics and Geneva Bioinformatics (GeneBio) SA for the analysis of flow cytometry data.
MegaClust present at DIA EuroMeeting 2013 via its partner Quartz Bio
Amsterdam, March 4-6. Do not hesitate to visit Quartz Bio booth #643 to get information on MegaClust.
SIB, Quartz Bio to Improve MegaClust's Flow Cytometry Analysis Capabilities for Pharma
Quartz Bio and the Swiss Institute of Bioinformatics are working together to develop and validate MegaClust, SIB's platform for analyzing flow cytomery data, so that they can use it to provide analysis services to pharmaceutical companies.
Improved Analysis of Flow Cytometry
The SIB Swiss Institute of Bioinformatics, GeneBio, and Quartz Bio announced the establishment of a long-term collaboration to develop, use, and jointly promote the MegaClust platform for the analysis of flow cytometry data.
The SIB Swiss Institute of Bioinformatics, GeneBio and Quartz Bio Unveil a Collaboration on MegaClust
The SIB Swiss Institute of Bioinformatics, Geneva Bioinformatics (GeneBio) SA and Quartz Bio SA today announced the establishment of a long-term collaboration under which they will cooperate in order to develop, use and jointly promote MegaClust, the SIB platform for the analysis of flow cytometry data
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