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Your Environment. Your Health.

Shyamal D. Peddada, Ph.D.

Biostatistics & Computational Biology Branch

Shyamal Peddada,  Ph.D.
Shyamal D. Peddada, Ph.D.
Deputy Chief, Biostatistics & Computational Biology Branch and Senior Investigator
111 Tw Alexander Dr
David P Rall Building
Research Triangle Park, NC 27709
Tel 984-287-4586
shyamal.peddada@nih.gov

Research Summary

Shyamal Peddada is a Senior Investigator who leads the Constrained Statistical Inference Group within the Biostatistics and Computational Biology Branch. The group focuses on developing broadly applicable rigorous biostatistical methods that are inspired by biomedical research. Methods developed by Peddada’s group have applications to toxicology, epidemiology, various ‘omics data and others. In addition to methodological research, the group is engaged in various scientific collaborations in biomedical research. A major area of research interest is to understand the role of human microbiome in health and disease.

Constraints arise naturally in many scientific investigations either due to the underlying study design and scientific hypotheses of interest, such as in a dose response study or in a time course experiment; or due to the intrinsic characteristics of variables under investigation, such as the expression of a gene participating in a cell-division cycle or in the circadian clock; or due to the underlying technology, such as the sc RNA-seq, 16S or metagenomics microbiome data, and others. Statistical methods that exploit such constraints are substantially more powerful than routine unconstrained statistical methods such as the standard linear regression, ANOVA, logistic regression or standard non-parametric methods. Equivalently, the constrained statistical inference-based methods require substantially smaller sample size for the same power than the standard methods. Hence, they potentially require fewer biospecimens and are cost effective. More importantly, in many instances these constrained inference-based methods provide better scientific interpretation of the data than the standard methods.

Peddada’s group develops, parametric and non-parametric constrained inference-based methods in low as well as high dimensions and applies the resulting methodologies to a wide range of biomedical research projects. Some examples include gene expression studies in toxicology, microbiome studies related to infant gut, infectious diseases, chemical exposures, and others.

Several user-friendly and freely-downloadable software have been developed by Peddada’s group such as ANCOM, ANCOM-BC, ANCOM-BC2, SECOM, ORIOGEN, CLME, ORIOS, and others.

In addition to conducting methodological and collaborative research, as well as developing user-friendly software, Peddada is actively engaged in mentoring trainees at all levels. His trainees are enjoying successful careers at various universities, research institutions and industries.

Prior to joining BCBB, Dr. Peddada was a Senior Investigator and the Chief of Biostatistics and Bioinformatics Branch at NICHD/NIH (2020 – 2022), the Chair of the Department of Biostatistics (2017 – 2020). He was also a member of BCBB (2000 – 2017) and acting Chief of BCBB (2016 – 2017). Dr. Peddada is a Fellow of the American Statistical Association (ASA), an elected Member of the International Statistical Institute, and a recipient of numerous awards including the ASA’s Outstanding Statistical Applications Award.

Selected Publications

  1. Lin, H., Eggesbø, M., Peddada, SD. 2022. Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data. Nat Commun 13, 4946. [Full Text Lin, H., Eggesbø, M., Peddada, SD. 2022. Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data. Nat Commun 13, 4946.]
  2. Chen Y, Lin H, Cole M, Morris A, Martinson J, Mckay H, Mimiaga M, Margolick J, Fitch A, Methe B, Srinivas VR, Peddada SD, Rinaldo CR. 2021. Signature changes in gut microbiome are associated with increased susceptibility to HIV-1 infection in MSM. Microbiome. [Full Text Chen Y, Lin H, Cole M, Morris A, Martinson J, Mckay H, Mimiaga M, Margolick J, Fitch A, Methe B, Srinivas VR, Peddada SD, Rinaldo CR. 2021. Signature changes in gut microbiome are associated with increased susceptibility to HIV-1 infection in MSM. Microbiome.]
  3. Lin H., Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction. Nat Commun. 11(1):3514. [Full Text Lin H., Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction. Nat Commun. 11(1):3514.]
  4. Rueda C., Larriba Y., Peddada SD. 2019. Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences. Sci Rep. [Full Text Rueda C., Larriba Y., Peddada SD. 2019. Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences. Sci Rep.]
  5. Jelsema C, Kwok R, Peddada SD. 2019. Threshold knot selection for large-scale spatial models with applications to the Deepwater Horizon disaster. J. Statist. Comp. Simulation. [Full Text Jelsema C, Kwok R, Peddada SD. 2019. Threshold knot selection for large-scale spatial models with applications to the Deepwater Horizon disaster. J. Statist. Comp. Simulation.]
  6. Shockley KR, Gupta S, Harris SF, Lahiri SN, Peddada SD. 2019. Quality Control of Quantitative High Throughput Screening Data. Front Genet. [Full Text Shockley KR, Gupta S, Harris SF, Lahiri SN, Peddada SD. 2019. Quality Control of Quantitative High Throughput Screening Data. Front Genet.]
  7. Davidov O, Jelsema CM, Peddada SD. 2018. Testing for Inequality Constraints in Singular Models by Trimming or Winsorizing the Variance Matrix. J. Amer. Statist. Assoc. [Full Text Davidov O, Jelsema CM, Peddada SD. 2018. Testing for Inequality Constraints in Singular Models by Trimming or Winsorizing the Variance Matrix. J. Amer. Statist. Assoc.]
  8. Peddada SD. 2017. Seasonal change in the gut: The gut mictobiome of Hadza hunter-gatherers changes with the season. Science 357 (6353), 754-755. [Invited Perspective].
  9. Kaul A, Davidov O, Peddada SD. 2017. Structural zeros in high-dimensional data with applications to microbiome studies. Biostatistics 18, 422-433. [Abstract Kaul A, Davidov O, Peddada SD. 2017. Structural zeros in high-dimensional data with applications to microbiome studies. Biostatistics 18, 422-433.]
  10. Mandal M , Godfrey K , McDonald D , van Treuren W , Bjørnholt JV , Midvedt T, Moen B , Rudi K , Knight R , Peddada SD , Eggesbø M. 2016. Fat and vitamin intakes during pregnancy have stronger relations with a pro-inflammatory maternal microbiota than does carbohydrate intake. Microbiome 4(1):55. [Abstract Mandal M , Godfrey K , McDonald D , van Treuren W , Bjørnholt JV , Midvedt T, Moen B , Rudi K , Knight R , Peddada SD , Eggesbø M. 2016. Fat and vitamin intakes during pregnancy have stronger relations with a pro-inflammatory maternal microbiota than does carbohydrate intake. Microbiome 4(1):55.]
  11. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health and Disease, 26, 1 – 7. [Abstract Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health and Disease, 26, 1 – 7.]
  12. Davidov O, Peddada SD (2013). The linear stochastic order and directed inference for multivariate ordered distributions. Annals of Statistics 41(1):1-40.  [Abstract Davidov O, Peddada SD (2013). The linear stochastic order and directed inference for multivariate ordered distributions. Annals of Statistics 41(1):1-40. ]
  13. Fernandez MA, Rueda C, Peddada SD. 2012. Identification of a core set of signature cell cycle genes whose relative order of time to peak expression is conserved across species. Nucleic Acids Res, 40,2823-32. Epub 2011/12/03. doi: 10.1093/nar/gkr1077. [Abstract Fernandez MA, Rueda C, Peddada SD. 2012. Identification of a core set of signature cell cycle genes whose relative order of time to peak expression is conserved across species. Nucleic Acids Res, 40,2823-32. Epub 2011/12/03. doi: 10.1093/nar/gkr1077.]
  14. Rueda, C, Fernandez, M and Peddada, SD. 2009. Estimation of parameters subject to order restriction on a circle with application to estimation of phase angles of cell-cycle genes. J. American Statist. Assoc., 104, 338-347 [Abstract Rueda, C, Fernandez, M and Peddada, SD. 2009. Estimation of parameters subject to order restriction on a circle with application to estimation of phase angles of cell-cycle genes. J. American Statist. Assoc., 104, 338-347]
  15. Peddada, SD, Laughlin, S, Miner, K, Guyon, JP, Haneke, K, Vahdat, H, Semelka, R, Kowalik, A, Armao, D, Davis, B and Baird D. (2008). Growth of Uterine Leiomyomata Among Pre-menopausal Black and White Women. Proc. National Acad. Sci., 105, 19887-19892. [Abstract Peddada, SD, Laughlin, S, Miner, K, Guyon, JP, Haneke, K, Vahdat, H, Semelka, R, Kowalik, A, Armao, D, Davis, B and Baird D. (2008). Growth of Uterine Leiomyomata Among Pre-menopausal Black and White Women. Proc. National Acad. Sci., 105, 19887-19892.]

Complete List of Published Work in My Bibliography.

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