Dr. Arnold Saxton
Professor, Graduate Committee Chair
1981-1983 Ph.D. North Carolina State Univ., Raleigh Animal Science (Statistics Minor)
1978-1980 MS University of Washington, Seattle Fisheries
1973-1977 BS University of Miami, Florida Biology and Chemistry
1983 PhD North Carolina State University, Animal Genetics and Statistics
1983-1991 Asst/Assoc Prof, Dept. Experimental Statistics, Louisiana State University
1992-present University of Tennessee
1995-present Affiliate faculty, Intercollegiate Graduate Statistics Program
2001-present Affiliate faculty, Genome Science & Technology
Professional Interest: Statistics, statistical genomics, experimental design, systems biology, microarray data analysis, genetic improvement.
Statistics is an area of science focused on making objective scientific inferences from “noisy” research data. Noisy data has variation from a variety of sources that makes it difficult to decide what is real. As animal scientists, we want to know what diets increase growth, what medicines improve health, what management strategies or drugs improve pregnancy rates. But if a diet is fed to one pen, and those pigs are 2% heavier than other pens, is that due to the diet, or to other factors such as breed, crowding, age, sub-clinical disease, etc? Statistics provides a mathematical process to separate out the effects of diet, and measures our confidence in the result.
I am specifically interested in statistics applied to genetic questions, but also am interested in statistical problems across all of agriculture. If you enjoy computers and programming, if you have done well in math classes, if working indoors rather than outside is appealing, you might consider a statistically oriented career. There has been a long tradition of agricultural scientists making important contributions in the statistics arena. R. A. Fisher worked at an Ag. Research Station in England in the early 1900’s, and developed many of the fundamental statistical tools and concepts (Fisher Collected Works). C. R. Henderson was a dairy scientist at Iowa State University in the mid-1900’s who created mixed models, currently revolutionizing statistical methods (Henderson Contributions). And the next revolution is predicted to be Bayesian statistics, with extensive contributions from Dan Gianola,
a dairy scientist at the University of
Wisconsin (Bayesian animal breeding). You could be next!
The publications below give a small sampling of the range of my statistical research applications. In genetics, microarray technology that measures expression of all genes produces challenging data. A project currently is studying why protection against false conclusions fails for certain experiments. QTL studies have the objective of finding which genes are associated with traits of interest, like growth and health. New methods for such data are proposed almost yearly, making it a challenge to test them for advantages and disadvantages, as well as utilize them for scientific discovery. Once genes are identified, the next goal is to understand how they work together in biochemical networks to produce functional changes (a small network is illustrated here, each circle being a gene, possibly working with other genes). Ultimately systems biology will construct (statistical) models that describe the interrelationships of genes, proteins, metabolites, and tissues. This better understanding of how the whole animal operates should improve strategies for making our animals healthier and more productive.
- Jay JJ, Eblen JD, Zhang Y, Benson M, Perkins AD, Saxton AM, Voy BH, Chesler EJ and Langston MA.
A systematic comparison of genome scale clustering algorithms. Lecture Notes in Computer Science (2011) 6674: 416-427. 10.1007/978-3-642-21260-4_39
- Payton RR, Rispoli LA, Saxton AM, Edwards JL. Impact of Heat Stress Exposure during Meiotic Maturation on Oocyte, Surrounding Cumulus Cell, and Embryo RNA Populations. J Reprod Dev. 2011 Apr 9. http://www.ncbi.nlm.nih.gov/pubmed/21478651?dopt=Abstract
- Wadl PA, Saxton AM, Wang X, Pantalone VR, Rinehart TA and Trigiano RN. 2011. Simple Sequence Repeat (SSR) Markers Associated with Red Foliage in Cornus florida L. Molecular Breeding 27:409-416 10.1007/s11032-011-9551-4
- Moon HS, Eda S, Saxton AM, Ow D, Stewart CN Jr. (2011) An efficient and rapid transgenic pollen screening and detection method using flow cytometry. Biotechnology Journal 6(1): 118-123. 10.1002/biot.201000258
- Stewart TP, Kim HY, Saxton AM and Kim JH. 2010. Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (TALLYHO/JngJ x C57BL/6J) F2 mice. BMC Genomics 11:713
- Critzer FJ, D'Souza DH, Saxton AM and Golden DA. (2010) Increased transcription of the phosphate-specific transport system of Escherichia coli O157:H7 after exposure to sodium benzoate, J Food Prot 73(5), 819-824.
- Liu, L.; Ye, X. P.; Saxton, A. M. and Womac, A. (2010), Pretreatment of near infrared spectral data in fast biomass analysis, Journal of Near Infrared Spectroscopy 18(5), 317-331.
- Kim HY, Stewart TP, Wyatt BN, Siriwardhana N, Saxton AM, Kim, JH. (2010) Gene expression profiles of a mouse congenic strain carrying an obesity susceptibility QTL under obesigenic diets. Genes & Nutrition 5(3), 237-250. 10.1007/s12263-009-0163-0
- Lutz CG, Armas-Rosales AM, and Saxton AM. Genetic effects influencing salinity tolerance in six varieties of tilapia (Oreochromis) and their reciprocal crosses. Aquaculture Research 2010:1-11.
- Lynch RM, Naswa S, Rogers Jr. GL, Kania SA, Das S, Chesler EJ, Saxton AM, Langston MA, and Voy BH. 2010. Identifying genetic loci and spleen gene coexpression networks underlying immunophenotypes in BXD recombinant inbred mice. Physiological Genomics 41: 244-253. 10.1152/physiolgenomics.00020.2010
- Borate BR, Chesler EJ, Langston MA, Saxton AM and Voy BH. 2009. Comparison of threshold
selection methods for microarray gene co-expression matrices. BMC Research Notes 2:240
- Eblen, J.D., I. C. Gerling, A. M. Saxton, J. Wu, J. R. Snoddy and M. A. Langston. 2009. Graph
Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data. In Clustering Challenges in Biological Networks (S. Butenko, W. A. Chaovalitwongse and P. Pardalos, editors),
World Scientific. pp 207-222.
- Langston, MA, AD Perkins, AM Saxton, JA Scharff and BH Voy. 2008. Innovative Computational
Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation. The Computer Journal 51(1): 26-38.
- Wang, X.; Carré, W.; Saxton, AM and Cogburn, LA. 2007. Manipulation of thyroid status and/or GH injection alters hepatic gene expression in the juvenile chicken. Cytogenetic Genome Res 117(1-4): 174-188.
- Auge, RM, HD Toler, C Keunho and AM Saxton. 2007. Comparing contributions of soil versus root colonization to variations in stomatal behavior and soil drying in mycorrhizal Sorghum bicolor and Cucurbita pepo. J Plant Physiology 164: 1289-1299.
- Panthee, D.R., V.R. Pantalone, A.M. Saxton, D.R. West, and C.E. Sams. 2006. Genomic Regions Associated with Amino Acid Composition in Soybean. Molecular Breeding 17:79-89.
- Saxton, AM, MA Langston and BH Voy. 2006. Statistical Tools are Needed for Array Data. Proceedings of the American Statistical Association [CD-ROM], Alexandria, VA. (Joint Research Conference on Statistics in Quality, Industry and Technology, June 7-9, Knoxville, TN).
- Smiley, R.D., A.M. Saxton, M.J. Jackson, S.N. Hicks, L.G. Stinnett and E.E. Howell. 2004. Non-Linear Fitting of Bi-Substrate Enzyme Kinetic Data using SAS: Application to R67 Dihydrofolate Reductase. Analytical Biochemistry 334(1): 204-206.
- Peng, X, MA Langston, AM Saxton, N Baldwin, JR Snoddy. 2004. Detecting Network Motifs in Gene Co-expression Networks. Critical Assessment of Microarray Data Analysis V, Duke University, Nov 10-12, 2004.
- Saxton, A.M. (Editor) 2004. Genetic Analysis of Complex Traits Using SAS. SAS Institute, Inc. Cary, N.C.
- Saxton, A.M. 1998. A macro for converting mean separation output to letter groupings in Proc Mixed. In Proc. 23rd SAS Users Group Intl., SAS Institute, Cary, NC, pp 1243-1246.
- Saxton, A.M. INDCULL Version 3.0: 1989. Independent culling for two or more traits. J.
Heredity 80: 166-167.
Non-Linear Enzyme Kinetics
The NLINEK SAS macro analyzes various bi-substrate enzyme kinetic data, including models with inhibition. The nonlinear mixed model algorithm in SAS has been found to perform very well with complex models.
Download Example files:
Proc Mixed Mean Separation Formatting
All SAS macros have been combined into a collection, DANDA.SAS, which is available at http://dawg.utk.edu/ (look in the glossary). Newest versions of PDMIX800 and PDGLM will only be posted there, but the below is preserved for continuity.
This macro formats pair-wise differences from SAS Proc MIXED, created by the PDIFF option on the LSMEANS statement. The differences are used to create groups of similar means, represented by letters A, B, etc. Documentation is included as comments at the top of the program file.CITATION: Saxton, A.M. 1998. A macro for converting mean separation output to letter groupings in Proc Mixed. In Proc. 23rd SAS Users Group Intl., SAS Institute, Cary, NC, pp1243-1246. Nashville, TN, March 22-25.
Bi-Substrate Enzyme Kinetics
This SAS macro analyzes sub-saturating bi-substrate enzyme kinetic data, in which one substrate is held constant and the other is varied. Weighted regression is used to produce estimates of Km and Vmax from inverse rate - inverse substrate plots. Usage is described in comments at the top of the file. Citation: Smiley, RD, Hicks, SN, Stinnett, LG, Howell, EE and Saxton, AM. 2002. Bi-substrate kinetics using SAS computer software. Analytical Biochemistry 301(1): 153-156.