Detection of Minimal Residual Disease in B Lymphoblastic Leukemia Using viSNE
By Joseph A. DiGiuseppe, Michelle D. Tadmor, Dana Pe'er. 2015
46 citations recorded by the source corpus.
Minimal residual disease (MRD) following treatment is a robust prognostic marker in B lymphoblastic leukemia. However, the detection of MRD by flow cytometric immunophenotyping is technically challenging, and an automated method to detect MRD is therefore desirable. viSNE, a recently developed computational tool based on the t‐Distributed Stochastic Neighbor Embedding (t‐SNE) algorithm, has been shown to be capable of detecting synthetic “MRD‐like” populations of leukemic cells created in vitro, but whether viSNE can facilitate the immunophenotypic detection of MRD in clinical samples has not been evaluated.
Subfields: Hematology, Molecular Biology, Public Health, Environmental and Occupational Health
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In Hematology
In Molecular Biology
- J. Snyder-Cappione
- Josef Spidlen
- Eirini Arvaniti
- Anna C. Belkina
- Manfred Claassen
- Blaz Zupan
- Pavlin Gregor Policar
- Martin Stražar
In Public Health, Environmental and Occupational Health
Citations in the corpus
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, background): “Consequently, researchers often resort to either subsampling their data to the very limit of detection of rare populations 14 or to exporting speci fi c populations from their dataset, thus compromising the unbiased data analysis approach 15,16 .”
- Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, background): “…finding initial poor t-SNE visualizations from large datasets, researchers often resort to either subsampling their data to the very limit of detection of rare populations 12 or to exporting specific populations from their dataset, thus compromising the ‘unbiased’ data analysis approach 13,14 .”
- Sensitive detection of rare disease-associated cell subsets via representation learning (2016, methodology): “…detection algorithm 20 , constituting a quantifiable variant of visually inspecting condition-specific projection map differences (e.g., t-SNE maps 21,22 ); (2) logistic regression, support vector machine and random forest classifiers that take as input single-cell profiles; (3) Citrus 9 ; and (4)…”