ArticleBioinformatics (Oxford, England)2019
CytoBackBone: an algorithm for merging of phenotypic information from different cytometric profiles.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 15 citations in OpenAlex.
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- Human splenic myeloid derived suppressor cells: Phenotypic and clustering analysis.Cellular immunology · 2021Article
- High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.Cancers · 2020Article
- Characterization of Leukocytes From HIV-ART Patients Using Combined Cytometric Profiles of 72 Cell Markers.Frontiers in immunology · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
motivationFlow and mass cytometry are experimental techniques used to measure the level of proteins expressed by cells at the single-cell resolution. Several algorithms were developed in flow cytometry to increase the number of simultaneously measurable markers. These approaches aim to combine phenotypic information of different cytometric profiles obtained from different cytometry panels.
resultsWe present here a new algorithm, called CytoBackBone, which can merge phenotypic information from different cytometric profiles. This algorithm is based on nearest-neighbor imputation, but introduces the notion of acceptable and non-ambiguous nearest neighbors. We used mass cytometry data to illustrate the merging of cytometric profiles obtained by the CytoBackBone algorithm. AVAILABILITY AND IMPLEMENTATION: CytoBackBone is implemented in R and the source code is available at https://github.com/tchitchek-lab/CytoBackBone. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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