Teng Fei, PhD

Assistant Attending Biostatistician

Teng Fei, PhD

Assistant Attending Biostatistician
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Teng Fei

Education

Emory University

Current Research Interest

Dr. Fei’s research interests are in the development of compositional data analysis methods for high-dimensional microbiome data, prediction modeling methods with multi-modal data integration, and latent class analysis methods for longitudinal and time-to-event data. Dr. Fei collaborates closely with the Marcel van den Brink Lab (City of Hope), the Jonathan Peled Lab, the Roni Shouval Lab (the Precision Cellular Therapy Laboratory), the Molecular Microbiology Facility, and the Division of Hematologic Malignancies in supporting microbiome, multi-modal and complex clinical data analysis, and developing novel methods to address the gaps between existing tools and challenging research questions. Dr. Fei developed highly accessed open-source R packages, including tidycmprsk for competing risks data analysis, scBatch for batch effect adjustment of RNA-seq data, and FLORAL for high-dimensional variable selection of microbial features.

Publications

Selected peer-reviewed publications:

  1. Raj SS, Fei T, Fried S, Ip A, Fein JA, Leslie LA, Tomas AA, Leithner D, Peled JU, Corona M, Dahi PB, Danylesko I, Epstein-Peterson Z, Funnell T, Giralt SA, Jacoby E, Kedmi M, Landego I, Lin R, Parascondola A, Pascual L, Arias-Orozco N, Park JH, Palomba ML, Salles G, Saldia A, Schöder H, Sdayoor I, Shah GL, Scordo M, Shem-Tov N, Shimoni A, Slingerland J, Yerushalmi R, Nagler A, Greenbaum BD, Vickers AJ, Suh HC, Avigdor A, Perales MA, van den Brink MRM, Shouval R. An inflammatory biomarker signature of response to CAR-T cell therapy in non-Hodgkin lymphoma. Nature Medicine. Accepted.
  2. Fei T, Funnell T, Waters NR, Raj SS, Baichoo M, Sadeghi K, Dai A, Miltiadous O, Shouval R, Lv M, Peled JU, Ponce DM, Perales MA, Gönen M, van den Brink MRM. Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL. Cell Report Methods. 2024 Nov 18;4(11):100899. doi: 10.1016/j.crmeth.2024.100899. Epub 2024 Nov 7. PubMed PMID: 39515336.
  3. Fei T, Hanfelt JJ, Peng L. Latent Class Proportional Hazards Regression with Heterogeneous Survival Data. Statistics and Its Interface. 2024;17(1):79-90. doi: 10.4310/23-sii785. Epub 2023 Nov 27. PubMed PMID: 38222248; PubMed Central PMCID: PMC10786342.
  4. Hart KR, Fei T, Hanfelt JJ. Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics. 2021 Sep;77(3):1118-1128. PMCID: PMC7937764.
  5. Fei T, Yu T. scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment. Bioinformatics. 2020 May 1;36(10):3115-3123. PMCID: PMC7214039.

Disclosures

Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Teng Fei discloses the following relationships and financial interests:

No disclosures meeting criteria for time period


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This page and data include information for a specific MSK annual disclosure period (January 1, 2023 through disclosure submission in spring 2024). This data reflects interests that may or may not still exist. This data is updated annually.

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