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Shyamal D. Peddada, Ph.D.

Biostatistics Branch

Shyamal D. Peddada, Ph.D.
Shyamal D. Peddada, Ph.D.
Principal Investigator
Tel (919) 541-1122
Fax (919) 541-4311
peddada@niehs.nih.gov
Curriculum Vitae (89KB)
P.O. Box 12233
Mail Drop A3-03
Research Triangle Park, North Carolina 27709
Delivery Instructions


The research program, headed by Shyamal Peddada, Ph.D., has two major components — methodological and subject matter research. The statistical methods developed in this program are used to analyze data obtained from a wide range of contexts including the following areas:

 

  • Analysis of microarray data
  • Cell-cycle gene expression studies
  • Dose-finding studies
  • High Throughput Screening (HTS) assays
  • Incorporation of historical control data when analyzing rodent cancer bioassay
  • Linear and nonlinear models
  • Physiologically based pharmacokinetic (PBPK) models
  • Standard two-year rodent cancer bioassay

 

Some major areas of research conducted in this program are summarized below.

 

Constrained Inference in the Euclidean space: Researchers are often interested in drawing inferences on unknown population parameters when the parameters are constrained by inequalities. For example, a cancer biologist may be interested in understanding changes in gene expression over “ordered conditions,” such as exposure to different doses and/or duration of exposure to a chemical, tumor stages etc. It may not always be feasible to develop parametric models to summarize patterns of response, but it is possible to use mathematical inequalities to describe the patterns of response. To this end, Peddada is developing methods for analyzing data that exploit the underlying inequalities among parameters that often result in powerful statistical methods. The constrained inference based methodology developed in this research program provides simple solutions without making complicated modeling assumptions (Peddada et al., JASA, 2007).

 

Constrained Inference on Unit Circle – Cell Cycle Experiments: In some instances the inequality constraints may arise naturally on a unit circle instead of the p-dimensional Euclidean space. For instance, cell-cycle experiments are routinely conducted to determine, among other things, the phase angle associated with each cell-cycle gene. In this case the parameter space is described by points on a unit circle. Based on available literature and known biological functions of cell-cycle genes, one may expect an (isotropic) order among the phase angles around the unit circle. New statistical methods are being developed in this research program for analyzing cell-cycle and circadian rhythm data (Rueda, Fernandez and Peddada, JASA, 2009).

 

Multiple testing in high dimensional data: Scientists routinely conduct analysis of high dimensional data. For example, toxicologists interested in studying the effects of a toxin on a tissue or a cell conduct dose-response microarray studies to compare different dose groups in terms of the expression of thousands of genes. As another example, toxicologists conduct high throughput screening assays to screen thousands of chemicals to determine toxic chemicals in a single study/experiment. In all such instances, researchers are faced with the problem of performing thousands of statistical tests, known as multiple testing. When performing a large number of statistical tests, there is a high probability of finding false positives just by chance. The challenge is to devise statistical methods that control the overall number of false positives. In this research program statistical methods are being developed which control the false discovery rate when analyzing high dimensional data.

 

Statistical Inference for dynamic systems: Stochastic and deterministic differential equations are used to describe a wide variety of biological and physiological phenomena. For example, physiologically based pharmacokinetic (PBPK) models use differential equations to explain the absorption, distribution, metabolism and excretion of a compound in humans and animals. A common challenge with these problems is the lack of explicit models that relate response and explanatory variables. Recent advances in functional data analysis provide useful approaches to solve these problems. Peddada and his pre-doctoral student are developing statistical methods for drawing inferences for compartmental and state space models with applications to PBPK models in toxicology.

 

Uterine Fibroid Growth Study: Uterine fibroids are very common among women of almost all ethnic backgrounds. According to some estimates, about 70 percent of women may have a uterine fibroid. Uterine fibroids are smooth muscle benign tumors that are believed to be hormonally mediated. Peddada and his colleagues are investigating factors associated with fibroid growth (Peddada et al., PNAS, 2008).

Group Members

Sidhartha Mandal

Predoctoral Visiting Fellow

 

Laura Lapkauskaite

Predoctoral student
UNC-Chapel Hill


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Software

Additional downloads at the Biostatistics software page ("/Rhythmyx/assembler/render?sys_contentid=56373&sys_revision=6&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56373" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56373" sys_siteid="" sys_folderid="").

 

  • Circular FSA
     ("/Rhythmyx/assembler/render?sys_contentid=51492&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="51492" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="51492" sys_siteid="" sys_folderid="")A SAS extention that tests whether the orthologs {B1,B2,...,BG} satisfy the same relative order as the genes { A1,A2,...,AG }.
  • ORIOGEN v 3 - Order Restricted Inference for Ordered Gene Expression
     ("/Rhythmyx/assembler/render?sys_contentid=35065&sys_revision=8&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35065" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35065" sys_siteid="" sys_folderid="")Analyzes gene expression data obtained from time-course/dose-response studies.
  • PCA-based Gene Filtering
     ("/Rhythmyx/assembler/render?sys_contentid=35076&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35076" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35076" sys_siteid="" sys_folderid="")A new filtering statistic for Affymetrix GeneChips, based on principle component analysis (PCA) on the probe-level gene expression data.
  • R code for fitting Random Periods Model ("/Rhythmyx/assembler/render?sys_contentid=51500&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="51500" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="51500" sys_siteid="" sys_folderid="")
    Software for fitting the Random Periods Model (RPM) for cell cycle genes.


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Selected Publications

  1. Hwang, JTG and Peddada, SD. (1994). Confidence Interval Estimation Subject to Order Restrictions. Annals of Statistics, 22, 67-93.
  2. Peddada, SD, Lobenhofer, L, Li, L, Afshari, C, Weinberg, C and Umbach, D. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics, 19, 834-841. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/12724293) ]
  3. Liu, D, Umbach, D, Peddada, SD, Li L, Crockett P and Weinberg C. (2004). A Random-Periods Model for Expression of Cell-Cycle Genes. PNAS, 101, No. 19, 7240-7245. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/15123814) ]
  4. Liu, D, Weinberg, C and Peddada, SD. (2004). A Geometric Approach to Determine Association and Coherence of the Activation Times of Cell-Cycling Genes under Different Experimental Conditions. Bioinformatics, 20, 2521-2528. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/15087309) ]
  5. Peddada, SD, Dinse, G and Haseman, J. (2005). A Survival-adjusted Quantal Response Test for Comparing Tumor Incidence Rates.  J. Royal Statist. Soc., Ser – C, 54, 51-61.
  6. Peddada, SD, Dunson, DB and Tan, X. (2005).  Estimation of order-restricted means from correlated data. Biometrika, 92, 703-715.  
  7. Peddada, SD, Harris, S, Zajd, J and Harvey E. (2005). ORIOGEN: Order Restricted Inference for Ordered Gene Expression Data.  Bioinformatics, 21, 3933-3934. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/16109745) ]  
  8. Peddada, SD and Kissling, G. (2005). A Survival-Adjusted Quantal-Response Test for Analysis of Tumor Incidence Rates in Animal Carcinogenicity Studies. Environmental Health Perspectives, 114, 537-541. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/16581542) ]  
  9. Peddada, SD, Haseman, J, Tan, X and Travlos G. (2006). Tests for Simple Tree Order Restriction with Application to Dose-response Studies. J. Royal Statist. Soc., Ser - C, 55, 493-506.  
  10. Peddada, SD, Dinse, G and Kissling G. (2007). Incorporating Historical Control Data When Comparing Tumor Incidence Rates. J. Amer. Stat. Assoc., 102, 1212-1220. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/20396669) ]  
  11. 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 (http://www.ncbi.nlm.nih.gov/pubmed/19047643) ]  
  12. 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 (http://www.ncbi.nlm.nih.gov/pubmed/19750145) ]  
  13. Guo, W, Sarkar, SK and Peddada, SD. (2009). Controlling False Discoveries in Multidimensional Directional Decisions, with Applications to Gene Expression Data on Ordered Categories. Biometrics, 66, 485-492. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/19645703) ]  
  14. Betcher, J, and Peddada, SD. (2009). Statistical inference under order restrictions in analysis of covariance using a modified restricted maximum likelihood estimator. Sankhya, Ser.-B, 71, 79-96. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/20957069) ]  
  15. Peddada, SD, Harris, S and Davidov, O. (2010). Analysis of Correlated Gene Expression Data on Ordered Categories. J. Ind. Soc. Agric. Statist., 64, 45-50.  
  16. Lim, C, Sen, PK and Peddada, SD. (2010). M-estimation methods in heteroscedastic nonlinear regression models (in press).  
  17. Baird, DD, Davis, B, and Peddada, SD. (2010). letter regarding: Cellular senescence in usual type uterine leiomyoma. Fertility and Sterility, 94(2):e43-. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/20605141?dopt=Abstract) ]  
  18. Baird, DD, Garrett, TA, Laughlin, SK, Davis, B, Semelka, RC and Peddada, SD. (2011). Short-term change in growth of uterine leiomyoma: tumor growth spurts. Fertility and Sterility, 95(1):242-246. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/21168581?dopt=Abstract) ]  
  19. Cao, Y, Chen, A, Jones, RL, Radcliffe, J, Dietrich, KN, Caldwell, KL, Peddada, SD, Rogan, WJ. (2011). Efficacy of succimer chelation of mercury at background exposures in toddlers: A randomized trial. The Journal of Pediatrics, 158(3):480-485. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/20889164?dopt=Abstract) ]  
  20. Dinse, GE, and Peddada, SD. (2011). Comparing tumor rates in current and historical control groups in rodent cancer bioassays. Statistics in Biopharmaceutical Research, 3(1):97-105. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/21566728?dopt=Abstract) ]  
  21. Perdivara I, Peddada, SD, Miller FW , Tomer KB, Deterding LJ. (2011). Mass spectrometric determination of IgG subclass-specific glycosylation profiles in siblings discordant for myositis syndromes. Journal of proteome research, 10(7):2969-2978. [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/21609021?dopt=Abstract) ]
  22. Lu J, Kerns RT, Peddada S, Bushel PR. (2011). Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays. Nucleic Acids Research, 39(13):e86. [Abstract (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=21525126) ]  
  23. Hoenerhoff, MJ, Pandiri, AP, Lahousse, SA, Hong, HH, Ton, TV, Masinde, T, Auerbach, S, Gerrish, K, Bushel, PR, Shockley, KR, Peddada, SD, Sills, RC. (2011). Global gene profiling of spontaneous hepatocellular carcinoma in B6C3F1 mice: similarities in the molecular landscape with human liver cancer. Toxicologic pathology, 39, 678-699. [Abstract (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=21571946) ]


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Last Reviewed: December 07, 2011