Naomi Altman

Dept. of Statistics
Huck Institute of Life Sciences


New Researcher's Guide (IMS)

Points of Significance Articles

Summary of research interests

Dr. Altman's interest in statistics stems from her broad interests in the application of the mathematical sciences to problems in other disciplines � in particular, medical and biological sciences, earth and environmental sciences, and social sciences. Her statistical interests include bioinformatics, high dimensional data, nonparametric smoothing, model selection and analysis of functional and longitudinal data. Dr. Altman's current research is in bioinformatics and dimension reduction. 

Dr. Altman's bioinformatics work includes the design and analysis of microarray and RNA-seq studies, functional genomics and gene clustering (by position on the chromosome, by sequence structure, and by function). Much of this work is currently in collaboration with biologists such as Claude dePamphilis (plants) and Iliana Baums (coral).  She also works in more applied areas such as social insects with Christina Grozinger and plant pathology with Tim McNellis. In medical bioinformatics, Dr. Altman has been working on single cell RNA-seq for circulating tumor cells with Gary Clawson,  the analysis of microbiome data and on the analysis of protein microarrays for antigen analysis.

Altman's work in functional data analysis and nonparametric smoothing has focused on problems in which the errors are correlated, and parametric covariate effects are of interest. Current areas of interest include inference for self-modeling regression when curves are the response in a comparative experiment and fitting and inference for longitudinal and spatial data with a smooth component.

Altman's work in high dimensional data has taken two directions.  She is working on multiple testing and other high dimensional estimation and testing problems which occur in parallel estimation and testing situations such as the analysis of `omics' data.  She also works on dimension reduction focusing on extensions of supervised and unsupervised methods based on matrix decompositions. 

Altman is a member of the Clinical and Translational Sciences Institute and is member of the core faculty in the Biostatistics, Epidemiology and Research Design group, which provides consulting services for biomedical research at Penn State.

Altman is also a member of the Huck Institutes of Life Sciences specializing in Bioinformatics and Genomics.  She is member of the core faculty on our new Computation, Bioinformatics, and Statistics (CBIOS) Training Program

Dr. Altman has directed 14 MS theses and directed or co-directed 5 Ph.D. theses, as of 2015.  She serves on about 10 graduate dissertation committees annually.

Nature Methods Articles

Altman is co-author, with the incredible Martin Krzywinski and occasionally others, of the Points of Significance Articles. in Nature Methods .  These articles cover a number of topics in statistics which should be useful to biologists and bioinformaticians.  it is part of the Nature Publishing Reproducibility Initiative

There is a companion set of articles on creating good graphics Points of View.  Altman not a co-author but highly recommends these articles.


Stat 414  Introduction to Probability
Stat 440 Statistical Computing (undergrad)
Stat 503 Experimental Design 2
Stat 511 Applied Linear Regression
Stat 512 Design of Experiments
Stat 540 Computationally Intensive Statistical Inference
Stat 555 Statistical Analysis of High Throughput Biology Experiments
Stat 580 Statistical Computing
Stat 597C Computing Environments for Statistics
Stat/Bio/CSE 598D Bioinformatics II - Microarrays
Stat/IBIOS 598A Current Research in Statistical Genomics

Talks and Lectures

Bioinformatics Talks
    Irreproducible Research and Data Bloat
    Differential Expression in Sequence Data
    Reproducible Research
    Gene Expression for Quantitative Scientists
    Role of Bioinformatics Center
    Designing Bioinformatics Studies
    The Biology, Technology and Statistical Modeling of High-throughput Genomics Data
    Resolving Isoform Expression using Digital Gene Expression Data
    more Talks

Statistics Talks
  Generalizing Principal Components Analysis
  Interpreting and Extending PCA (in meteorology)
  Statistics: Taskmistress or Temptress?
    Estimating Pi0 and FDR for Discrete Tests
  Self-Modeling Regression for Longitudinal Data
  Nonparametric Regression for Longitudinal Data
  Confidence Sets for Clusters
more Talks


Complete list of publications in CV

Representative publications: Statistics

Dialsingh, I., Austin, S. and Altman, N.S.  (2015) Estimating the Percentage of True Null Hypotheses when the Statistics are Discrete.  Bioinformatics doi:10.1093/bioinformatics/btv104 oxfordJournals

 Stefanie R. Austin, Isaac Dialsingh, Naomi Altman.((2014)  Multiple Hypothesis Testing: A review.  J. Indian Soc. Of Agricultural Stat. 68: 303-314.pdf

Luo, W. and Altman, N. S.   (2013) A Characterization of Conjugate Priors in Linear Exponential Families with application to Dimension Reduction.  Statistics and Probability Letters, 83, 650-654.sciencedirect

Li, B., Kim, M.K. and Altman, N.S.  (2010) On dimension folding of matrix or array valued statistical  objects.  Annals of Statistics,38, 1094-1121;

Altman, N.S. and J. Villarreal. (2004). Self-modeling regression with random effects using penalized splines, Canadian Journal of Statistics, 32.jstor

Altman, N.S. (2000). Krige, smooth, both or neither? (with discussion). Australian and New Zealand Journal of Statistics 42: 441-461. Wiley

Altman, N.S. and C. Leger. (1997). On the optimality of prediction-based selection criteria and the convergence rates of estimators. Journal Royal Statistical Society, Series B 59: 205-216.jstor

Altman, N.S. and G. Casella.(1995) Nonparametric empirical Bayes growth curve analysis. JASA 90: 508-515.jstor

L�ger, C. and Altman, N.S., (1993)  Assessing Influence in Variable Selection  Problems.  Journal of the American Statistical Association, 88, 547-556.jstor

Altman, N.S, (1990) Kernel Smoothing of Data with Correlated Errors.   Journal of the American Statistical Association, 85, 749-758   jstor

Representative publications: Bioinformatics

Honaas, L., Altman, N.S. and Kryzwinski, M. (2015) Study Design for Sequencing Studies.  in Methods in Statistical Genomics. Mathe, E. and Davis, S. (editors). Springer. (in press)

Zhenzhen Yang, Eric K. Wafula, Loren A. Honaas, Huiting Zhang, Malay Das, Monica Fernandez-Aparicio, Kan Huang, Pradeepa C.G. Bandaranayake, Biao Wu, Joshua P. Der, Christopher R. Clarke, Paula E. Ralph, Lena Landherr, Naomi S. Altman, Michael P. Timko, John I. Yoder, James H. Westwood, and Claude W. dePamphilis.  (2014) Comparative transcriptome analyses reveal core parasitism genes and suggest gene duplication and repurposing as sources of structural novelty. Molecular Biology and Evolution.  DOI: 10.1093/molbev/msu343  oxfordjournals

Philip J Jensen, Gennaro Fazio, Naomi Altman, Craig Praul and Timothy McNellis.(2014) Mapping in an apple (Malus x domestica) F1 segregating population based on physical clustering of differentially expressed genes.  BMC Genomics 15:261

Amborella Genome Project(2013) "The Amborella Genome and the Evolution of Flowering Plants�  Science.    20: 1241089 [DOI:10.1126/science.1241089] science

Smyth, G.K. and Altman, N.S. (2013) Separate-Channel Analysis of Two-Channel Microarrays: recovering inter-spot information.  BMC Bioinformatics 14:165  doi:10.1186/1471-2105-14-165 BMC

Zahn, LM, Ma,X, Altman, NS, Zhang, Q, Wall, PK, Tian, D., Gibas, CJ, Gharaibeh, R, Leebens-Mack, JH, dePamphilis, CW and Ma, H. (2010) Comparative transcriptomics among floral organs of the basal eudicot Eschscholzia californica as reference for floral evolutionary developmental studies. Genome Biology, 11:R101. 

Altman, N.S., Wang, Q., Karwa, V. and Slavkovic, S.  (2010) Resolving Isoform Expression using Digital Gene Expression Data.  (in press, Journal of the Indian Society of Agricultural Statistics, special issue on Statistical Genomics.)

Altman, N.S. (2009) Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies. in Batch Effects and Noise in Microarray Experiments: Sources and Solutions. A. Scherer (editor).  John Wiley & Sons, Chichester.

Han, X., X. Wu, W.-Y. Chung, T. Li, A. Nekrutenko, N. Altman, G. Chen, and H. Ma, ((2009)  Transcriptome of embryonic and neonatal mouse cortex by high-throughput RNA sequencing. Proceedings of the National Academy of Sciences, vol. 106, no. 31, pp. 12741-6. PNAS

Wall, P.K.,  J. H. Leebens-Mack, A. Barakat, A. Chanderbali, L. Landherr, N. Altman, J. E. Carlson, H. Ma, W. Miller, S. Schuster, D.E. Soltis, P.S. Soltis, and C.W. dePamphilis. (2008) Comparison of next generation sequencing technologies for de novo transcriptome characterization.  BMC Genomics. BMC Genomics

Han, B., Altman, N.S., Mong, J.A., Klein, L.C., Pfaff, D.W. and Vandenbergh, D. (2008) Comparing Quantitative Trait Loci and Gene Expression Data Associated with a Complex Trait, Advances in Bioinformatics. Hindawi Press

P. Kerr Wall, Jim Leebens-Mack, Kai M�ller, Dawn Field, Naomi S. Altman, Claude W. dePamphilis. (2007) PlantTribes:  A gene and gene family resource for comparative genomics in plants.  Nucleic Acid Research, 36, 970-976. NAR

Soltis D.E., H. Ma, M.W. Frohlich, P.S. Soltis, V.A. Albert, D.G. Oppenheimer, N.S. Altman, C.W. dePamphilis and J.H. Leebens-Mack. (2007) The floral genome: an evolutionary history of gene duplication and shifting patterns of gene expression. Trends in Plant Science 12(8):358-367.ScienceDirect

Altman, N.S., Hua, J. (2006) Extending the loop design for 2-channel microarray experiments Genetical Research, Vol 88, No. 3, p. 153-163.Cambridge

Altman, N.S. (2005). Replication, variation and normalization in microarray experiments Applied Bioinformatics, 4, 33-44.pdf

Last updated: 8 Aug 2015