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Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays
Author(s) -
Nima Aghaeepour,
Pratip K. Chattopadhyay,
Anuradha Ganesan,
Kieran O’Neill,
Habil Zare,
Adrin Jalali,
Holger H. Hoos,
Mario Roederer,
Ryan R. Brinkman
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts082
Subject(s) - bioconductor , multivariate statistics , multivariate analysis , flow cytometry , identification (biology) , computer science , computational biology , documentation , biology , data mining , immunology , machine learning , genetics , botany , gene , programming language
Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.

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