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Bioinformatics. 2019 Mar 23. pii: btz212. doi: 10.1093/bioinformatics/btz212. [Epub ahead of print]

CytoBackBone: an Algorithm for Merging of Phenotypic Information from Different Cytometric Profiles.

Author information

CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.
APHP, Hôpitaux Universitaires Paris Sud, Service de Médecine Interne-Immunologie Clinique, Le Kremin-Bicêtre, France.
Université Paris Sud, Le Kremlin-Bicêtre, France.



Flow 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.


We 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.


CytoBackBone is implemented in R and the source code is available at

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