VoPo

"VoPo" is a machine learning algorithm for managing and interpreting the vast amounts of data generated by high-throughput single-cell analysis technologies. These technologies are pivotal for understanding cellular heterogeneity, which is critical for comprehending the complex behavior of cellular systems in health and disease. VoPo offers predictive modeling capabilities and comprehensive visualization tools tailored explicitly for large single-cell datasets.

Key Features and Capabilities:

- Identification of Cell Populations: VoPo excels in defining phenotypically and functionally homogeneous cell populations within the immense complexity of cellular systems, as demonstrated across three mass cytometry datasets. The largest of these datasets encompassed hundreds of millions of cells across hundreds of samples, highlighting VoPo's capacity to handle extensive data.

- Immune-correlates Identification: VoPo has been used to identify immune-correlates of clinically relevant parameters. This feature is particularly valuable in biomedical research and healthcare, where understanding the relationship between immune system behavior and clinical outcomes can lead to significant advances in diagnostics and therapeutics.

Topic

Cytometry;Immunology;Genotype and phenotype;Surgery;Machine learning

Detail

  • Operation: Visualisation;Dimensionality reduction;Clustering

  • Software interface: Command-line interface

  • Language: R

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: The March of Dimes Prematurity Research Center at Stanford and the Bill and Melinda Gates Foundation, the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, Burroughs Wellcome Fund, National Institute of Health, U.S. FDA, Stanford Immunology Training Grant, the Stanford Anesthesia Training Grant, the German Research Foundation, the National Institute of Health, the Doris Duke Charitable Foundation, Stanford Maternal and Child Health Research Institute, the Charles and Mary Robertson Foundation, the American Heart Association.

  • Input: -

  • Output: -

  • Contact: Nima Aghaeepour naghaeep@stanford.edu

  • Collection: -

  • Maturity: -

Publications

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.
  • Stanley N, et al. VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. 2020; 11:3738. doi: 10.1038/s41467-020-17569-8
  • https://doi.org/10.1038/S41467-020-17569-8
  • PMID: 32719375
  • PMC: PMC7385162

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