Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic <scp>Flow‐Self</scp> Organizing Maps algorithm-Reference-Cited by-同舟云学术

Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow‐Self Organizing Maps algorithm

Published:2022-02-12 Issue:2 Volume:102 Page:134-142
ISSN:1552-4949
Container-title:Cytometry Part B: Clinical Cytometry
language:en
Short-container-title:Cytometry Part B Clinical

Author:

Porwit Anna¹²^ORCID,Violidaki Despoina¹²^ORCID,Axler Olof¹²,Lacombe Francis³,Ehinger Mats¹²,Béné Marie C.⁴^ORCID

Affiliation:

1. Department of Clinical Sciences, Oncology and Pathology Lund University, Faculty of Medicine Lund Sweden

2. Department of Clinical Genetics and Pathology Skåne University Hospital Lund Sweden

3. Hematology Biology Bordeaux University Hospital Haut Leveque Bordeaux France

4. Hematology Biology Nantes University Hospital & CRCINA Nantes France

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.b.22059

Reference20 articles.

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2. Definition of erythroid differentiation subsets in normal human bone marrow using FlowSOM unsupervised cluster analysis of flow cytometry data;Béné M. C.;Hema,2020

3. Clinical Utility of Flow Cytometry in the Study of Erythropoiesis and Nonclonal Red Cell Disorders

4. Minimal morphological criteria for defining bone marrow dysplasia: a basis for clinical implementation of WHO classification of myelodysplastic syndromes

5. Computational flow cytometry as a diagnostic tool in suspected‐myelodysplastic syndromes

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