Article

VISDA: an open-source caBIGTM analytical tool for data clustering and beyond

Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
Bioinformatics (Impact Factor: 4.62). 09/2007; 23(15):2024-7. DOI: 10.1093/bioinformatics/btm290
Source: PubMed

ABSTRACT VISDA (Visual Statistical Data Analyzer) is a caBIG analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical datasets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data. AVAILABILITY: http://gforge.nci.nih.gov/projects/visda/

Download full-text

Full-text

Available from: Yitan Zhu, Aug 27, 2015
0 Followers
 · 
157 Views
  • Source
    • "Zheng and colleagues (Yin et al., 2008) introduce an incremental clustering-based phenotype identification method with established pre-categorised cell images as one of its initial conditions. Wang et al. (2007), in a similar spirit as our proposed method, describe a clustering approach accompanied by GUI visualisation for model selections. Like the other methods discussed so far, Wang's method assumes the existence of initial phenotype labels. "
  • Source
    • "Zheng and colleagues (Yin et al., 2008) introduce an incremental clustering-based phenotype identification method with established pre-categorised cell images as one of its initial conditions. Wang et al. (2007), in a similar spirit as our proposed method, describe a clustering approach accompanied by GUI visualisation for model selections. Like the other methods discussed so far, Wang's method assumes the existence of initial phenotype labels. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Most phenotype-identification methods in cell-based screening assume prior knowledge about expected phenotypes or involve intricate parameter-setting. They are useful for analysis targeting known phenotype properties; but need exists to explore, with minimum presumptions, the potentially-interesting phenotypes derivable from data. We present a method for this exploration, using clustering to eliminate phenotype-labelling requirement and GUI visualisation to facilitate parameter-setting. The steps are: outlier-removal, cell clustering and interactive visualisation for phenotypes refinement. For drug-siRNA study, we introduce an auto-merging procedure to reduce phenotype redundancy. We validated the method on two Golgi apparatus screens and showcase its contribution for better understanding of screening-images.
    International Journal of Computational Biology and Drug Design 01/2011; 4(2):194-215. DOI:10.1504/IJCBDD.2011.041011
  • Source
    • "Moreover, cloud resources could be implemented on top of existing infrastructures dedicated to routine office tasks in public health departments or similar organizations. Similar work reported in the literature includes the Visual Statistical Data Analyzer (VISDA), a grid-based analytical tool [2] [3] that includes spatial analyses, and work done using the Open-Source Grid-Computing technology to improve processing time for geospatial syndromic surveillance [4]. Both projects illustrated the value of grid computing in spatial analysis. "
    [Show abstract] [Hide abstract]
    ABSTRACT: By using cloud computing it is possible to provide on- demand resources for epidemic analysis using computer intensive applications like SaTScan. Using 15 virtual machines (VM) on the Nimbus cloud we were able to reduce the total execution time for the same ensemble run from 8896 seconds in a single machine to 842 seconds in the cloud. Using the caBIG tools and our iterative software development methodology the time required to complete the implementation of the SaTScan cloud system took approximately 200 man-hours, which represents an effort that can be secured within the resources available at State Health Departments. The approach proposed here is technically advantageous and practically possible.
    04/2010; 2(1). DOI:10.5210/ojphi.v2i1.2910
Show more