ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2015
Computational prediction of manually gated rare cells in flow cytometry data.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dual delivery of IL-10 and AT-RvD1 from PEG hydrogels polarize immune cells towards pro-regenerative phenotypes.Biomaterials · 2021Article
- Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.Blood advances · 2020Review
- Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Toward deterministic and semiautomated SPADE analysis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection.BMC medical genomics · 2016Article
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Authors and funding
1 author.
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Abstract
Rare cell identification is an interesting and challenging question in flow cytometry data analysis. In the literature, manual gating is a popular approach to distill flow cytometry data and drill down to the rare cells of interest, based on prior knowledge of measured protein markers and visual inspection of the data. Several computational algorithms have been proposed for rare cell identification. To compare existing algorithms and promote new developments, FlowCAP-III put forward one computational challenge that focused on this question. The challenge provided flow cytometry data for 202 training samples and two manually gated rare cell types for each training sample, roughly 0.02 and 0.04% of the cells, respectively. In addition, flow cytometry data for 203 testing samples were provided, and participants were invited to computationally identify the rare cells in the testing samples. Accuracy of the identification results was evaluated by comparing to manual gating of the testing samples. We participated in the challenge, and developed a method that combined the Hellinger divergence, a downsampling trick and the ensemble SVM. Our method achieved the highest accuracy in the challenge.
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