Shyamal Peddada, Ph.D.
Biostatistics & Computational Biology Branch
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.
Shyamal Peddada is a Senior Investigator who leads the Constrained Statistical Inference Group within the Biostatistics and Computational Biology Branch (BCBB). 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.
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, 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). 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.
- Lin H, Eggesbø M, Peddada SD. Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data. Nat Commun. 2022;13(1):4946.
- Kaul A, Davidov O, Peddada SD. Structural zeros in high-dimensional data with applications to microbiome studies. Biostatistics. 2017;18(3):422-433.
- Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:27663.
- Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010;66(2):485-92.
- Peddada SD, Laughlin SK, Miner K, Guyon JP, Haneke K, Vahdat HL, Semelka RC, Kowalik A, Armao D, Davis B, Baird DD. Growth of uterine leiomyomata among premenopausal black and white women. Proc Natl Acad Sci U S A. 2008;105(50):19887-92.
Related Scientific Focus Areas
This page was last updated on Wednesday, January 11, 2023