Title |
Critical assessment of automated flow cytometry data analysis techniques
|
---|---|
Published in |
Nature Methods, February 2013
|
DOI | 10.1038/nmeth.2365 |
Pubmed ID | |
Authors |
Nima Aghaeepour, Greg Finak, Holger Hoos, Tim R Mosmann, Ryan Brinkman, Raphael Gottardo, Richard H Scheuermann |
Abstract |
Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 22% |
Canada | 3 | 13% |
Germany | 2 | 9% |
Japan | 2 | 9% |
France | 1 | 4% |
Italy | 1 | 4% |
Unknown | 9 | 39% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 57% |
Scientists | 10 | 43% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 14 | 2% |
Germany | 3 | <1% |
France | 3 | <1% |
Canada | 3 | <1% |
United Kingdom | 3 | <1% |
Spain | 2 | <1% |
Colombia | 1 | <1% |
Portugal | 1 | <1% |
Czechia | 1 | <1% |
Other | 5 | <1% |
Unknown | 692 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 183 | 25% |
Researcher | 172 | 24% |
Student > Master | 73 | 10% |
Student > Bachelor | 50 | 7% |
Other | 45 | 6% |
Other | 107 | 15% |
Unknown | 98 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 205 | 28% |
Biochemistry, Genetics and Molecular Biology | 77 | 11% |
Medicine and Dentistry | 72 | 10% |
Computer Science | 69 | 9% |
Immunology and Microbiology | 52 | 7% |
Other | 134 | 18% |
Unknown | 119 | 16% |