Experiment Overview
| Repository ID: | FR-FCM-ZYV2 | Experiment name: | CytoBackBone: An Algorithm to Merge Cytometric Profiles | MIFlowCyt score: | 79.50% |
| Primary researcher: | Nicolas Tchitchek | PI/manager: | Nicolas Tchitchek | Uploaded by: | Nicolas Tchitchek |
| Experiment dates: | 2017-06-01 - 2018-06-01 | Dataset uploaded: | Jan 2019 | Last updated: | Jan 2019 |
| Keywords: | [mass cytometry] [CytoBackBone] [cytometry profile merging] | Manuscripts: | |||
| Organizations: |
CEA-Université Paris Sud 11-INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases (IMVA), Fontenay-aux-Roses, (France)
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| Purpose: | Flow and mass cytometry are experimental techniques used to measure the level of proteins expressed by cells at the single-cell resolution. Several computational approaches have been 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. The CytoBackBone algorithm has been implemented in R, based on the flowCore, flowUtils, and FNN packages. The source code of CytoBackBone is available at https://github.com/tchitchek-lab/CytoBackBone. | ||||
| Conclusion: | Merging results produced by CytoBackBone are symmetrical and more-stringent compared to other approaches thanks to the notions of acceptable and non-ambiguous nearest neighbors. | ||||
| Comments: | The efficiency of the CytoBackBone algorithm was illustrated using whole blood samples from a healthy patient. Samples were stained either with a complete mass cytometry panel of 35 markers, or with one of the four incomplete mass cytometry panels. Incomplete antibody panels were derived by omitting several markers from complete panel, and were used to generate combined cytometric profiles. | ||||
| Funding: | The IDMIT infrastructure is supported by the French government “Programme d’Investissements d’Avenir” (PIA) under Grant ANR-11-INBS-0008 and grant ANR-10-EQPX-02-01 (FlowCyTech facility). NT was supported by fellowships from the ANRS (France Recherche Nord\&Sud Sida-HIV Hépatites). | ||||
| Quality control: | CyTOF QC was checked before each acquisition. | ||||
