Dept. of Statistics

Huck Institute of Life Sciences

Clinical and Translational Sciences Institute

Huck Institute of Life Sciences

Clinical and Translational Sciences Institute

CV

New Researcher's Guide (IMS)

Points of Significance Articles

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.

Teaching

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

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

Publications

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; arxiv.org

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 http://www.biomedcentral.com/1471-2164/15/261

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. http://genomebiology.com/2010/11/10/R101

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