Shyamal D. Peddada, Ph.D. | National Institute of Environmental Health Sciences
Skip Navigation
Shyamal D. Peddada, Ph.D.
Close the left navigation
Biostatistics & Computational Biology Branch
Table of Contents
Research Summary
Selected Publications
Shyamal D. Peddada, Ph.D.
Principal Investigator
Tel 984-287-4586
[email protected]
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
, and
ORIOS
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
Lin H,
Peddada SD
. 2023. Multi-group Analysis of Compositions of Microbiomes With Covariate Adjustments and Repeated Measures.
Nat Methods
21, 83—91. [
Full Text
Lin H, Peddada SD. 2023. Multi-group Analysis of Compositions of Microbiomes With Covariate Adjustments and Repeated Measures. Nat Methods 21, 83–91.
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.
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.
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.
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.
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.
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.
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.
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].
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.
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.
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.
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.
Peddada SD
, Laughlin SK, Miner K, Guyon JP, Haneke K, Vahdat HL, Semelka RC, Kowalik A, Armao D, Davis B, Baird DD (2008). Growth of uterine leiomyomata among premenopausal black and white women.
Proc Natl Acad Sci
U S A [
Abstract
Peddada SD, Laughlin SK, Miner K, Guyon JP, Haneke K, Vahdat HL, Semelka RC, Kowalik A, Armao D, Davis B, Baird DD (2008). Growth of uterine leiomyomata among premenopausal black and white women. Proc Natl Acad Sci U S A
Peddada SD
, Lobenhofer EK, Li L, Afshari CA, Weinberg CR, Umbach DM (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference.
Bioinformatics
. 19(7):834-41. [
Abstract
Peddada SD, Lobenhofer EK, Li L, Afshari CA, Weinberg CR, Umbach DM (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics. 19(7):834-41.
Hwang JTG,
Peddada SD
(1994). Confidence Interval Estimation Subject to Order Restrictions.
Annals of Statistics,
22
, 67-93. [
Full Text
Hwang JTG, Peddada SD (1994). Confidence Interval Estimation Subject to Order Restrictions. Annals of Statistics, 22, 67-93.
Complete List of Published Work in
My Bibliography
Back
to Top
Last Reviewed: December 12, 2025