Overview of clinical flow cytometry data analysis: recent advances and future challenges
Trends in Biotechnology, ISSN: 0167-7799, Vol: 31, Issue: 7, Page: 415-425
2013
- 116Citations
- 260Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations116
- Citation Indexes114
- CrossRef114
- 111
- Clinical Citations1
- PubMed Guidelines1
- Patent Family Citations1
- Patent Families1
- Captures260
- Readers260
- 260
Review Description
Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167779913000942; http://dx.doi.org/10.1016/j.tibtech.2013.04.008; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84879265763&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/23746659; https://linkinghub.elsevier.com/retrieve/pii/S0167779913000942; https://dx.doi.org/10.1016/j.tibtech.2013.04.008
Elsevier BV
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