An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Methods

Volumn 112, Issue , 2017, Pages 201-210

  Hennig, Holger ^a,b,c   Rees, Paul ^a,c   Blasi, Thomas ^d   Kamentsky, Lee ^a   Hung, Jane ^a   Dao, David ^a   Carpenter, Anne E ^a   Filby, Andrew ^e  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b UNIVERSITY OF ROSTOCK   (Germany)

^c SWANSEA UNIVERSITY   (United Kingdom)

^d INSTITUTE OF EPIDEMIOLOGY   (Germany)

^e NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Feature selection; High throughput; Imaging flow cytometry; Machine learning; Open source software; Profiling

Indexed keywords

ALGORITHM; ARTICLE; CELL CLASSIFIER; CELL CYCLE PHASE; CLASSIFICATION; CLASSIFIER; CONTROLLED STUDY; DATA ANALYSIS; FLOW CYTOMETRY; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGING FLOW CYTOMETRY; JURKAT CELL LINE; MACHINE LEARNING; MEASUREMENT ACCURACY; OPEN SOURCE SOFTWARE; PREDICTION; PRIORITY JOURNAL; SINGLE CELL ANALYSIS; SOFTWARE; CELL COUNT; GENETICS; HUMAN; IMAGE CYTOMETRY; INTERPHASE; MITOSIS; PROCEDURES; REPRODUCIBILITY; STATISTICS AND NUMERICAL DATA; WORKFLOW;

CELL COUNT; FLOW CYTOMETRY; HUMANS; IMAGE CYTOMETRY; IMAGE PROCESSING, COMPUTER-ASSISTED; INTERPHASE; JURKAT CELLS; MACHINE LEARNING; MITOSIS; REPRODUCIBILITY OF RESULTS; SOFTWARE; WORKFLOW;

EID: 84994481737     PISSN: 10462023     EISSN: 10959130     Source Type: Journal    
DOI: 10.1016/j.ymeth.2016.08.018     Document Type: Article

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