Nikolaos Ignatiadis
Ph.D. Student in Statistics, admitted Autumn 2016
Masters Student in Statistics, admitted Winter 2022
All Publications
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Empirical Bayes Mean Estimation With Nonparametric Errors Via Order Statistic Regression on Replicated Data
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
2021
View details for DOI 10.1080/01621459.2021.1967164
View details for Web of Science ID 000698937600001
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Covariate powered cross-weighted multiple testing
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
2021
View details for DOI 10.1111/rssb.12411
View details for Web of Science ID 000680936200001
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Covariate-Powered Empirical Bayes Estimation
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866901027
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Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
Nature communications
2019; 10 (1): 5587
Abstract
Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.
View details for DOI 10.1038/s41467-019-13441-6
View details for PubMedID 31811131