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  • Academic
    ignat@stanford.edu
    University - Student Department: Statistics Position: Graduate
    University - Student Department: Statistics Position: Graduate

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All Publications

  • Empirical Bayes Mean Estimation With Nonparametric Errors Via Order Statistic Regression on Replicated Data JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Ignatiadis, N., Saha, S., Sun, D. L., Muralidharan, O. 2021

    View details for DOI 10.1080/01621459.2021.1967164

    View details for Web of Science ID 000698937600001

  • Covariate powered cross-weighted multiple testing JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Ignatiadis, N., Huber, W. 2021

    View details for DOI 10.1111/rssb.12411

    View details for Web of Science ID 000680936200001

  • Covariate-Powered Empirical Bayes Estimation Ignatiadis, N., Wager, S., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000535866901027

  • Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. Nature communications Karacosta, L. G., Anchang, B. n., Ignatiadis, N. n., Kimmey, S. C., Benson, J. A., Shrager, J. B., Tibshirani, R. n., Bendall, S. C., Plevritis, S. K. 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

https://orcid.org/0000-0001-7767-2583