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Population Clustering for Analyzing Flow Cytometry and Other Large Data Sets


Stanford Reference:

02-215


Abstract


This patented, automated data analytics tool sorts and analyzes large data sets by identifying and creating clusters of data. The algorithm intakes data and then groups them into clusters, groupings, or populations of data. This nonparametric method has been used for identifying homogenous subpopulations of cells (i.e. gating) in flow cytometry data. The technique is also broadly applicable for data analysis in any other field that contains clusterable parameters, such as marketing, data mining, genomics, or financial software.

Applications


  • Flow cytometry analysis - gating to identify subpopulations of cells
  • Data analysis for:
    • Market research
    • Genomics - gene expression, functional genomics
    • Data mining
    • Financial software

Advantages


  • Automated and objective - not subject to user’s interpretation and experience
  • Fast - automatic gating for flow cytometry takes a fraction of a second
  • Non-parametric - can reproduce nonconvex subpopulations

Publications



Innovators & Portfolio



Patent Status



Date Released

 7/11/2011
 

Licensing Contact


Luis Mejia, Senior Licensing Associate
(650) 725-9409 (Direct)
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Related Keywords


FACS (Fluorescent Activated Cell Sorting)   analytics software   data analysis software   data analytics   flow cytometry software   genomics: bioinformatic   genomics: microarray software   high-dimensional data analysis   microarray software   software: bioinformatics   sequence analysis software   statistical analysis software   big data   bioinformatics   data mining   data analysis   computer: data mining   data mining software   Lab Management Software   
 

   

  

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S02-215 Population Clustering for Analyzing Flow Cytometry and Other Large Data Sets