Competitive SWIFT cluster templates enhance detection of aging changes

• Published in: Cytometry. Part A Vol. 89; no. 1; pp. 59 - 70
• Main Authors: Rebhahn, Jonathan A., Roumanes, David R., Qi, Yilin, Khan, Atif, Thakar, Juilee, Rosenberg, Alex, Lee, F. Eun‐Hyung, Quataert, Sally A., Sharma, Gaurav, Mosmann, Tim R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2016; John Wiley and Sons Inc
• Subjects: Adult; Aged; Aged, 80 and over; Aging; Algorithms; automated analysis; Biomarkers - analysis; Change detection; Cluster Analysis; Clustering; Clusters; Competition; competitive clustering; Computational Analysis of Flow Cytometry Data (PART II); Computational Biology - methods; Cytometry; Data Interpretation, Statistical; Datasets; EM algorithm; Female; Flow cytometry; Flow Cytometry - methods; Fluorescence; Geriatrics; Humans; immunophenotyping; Immunophenotyping - methods; Leukocytes, Mononuclear - cytology; Leukocytes, Mononuclear - immunology; Male; Middle Aged; Older people; sample comparison; Sex Factors; Sharpening; Subpopulations; SWIFT; template; Young Adult; template; immunophenotyping; competitive clustering; SWIFT; sample comparison; flow cytometry; automated analysis; EM algorithm

Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the impor...