Statistics and Computational Biology

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Timeline of methods developed in our group (click image for a larger view)

Our main research topics in biostatistics and bioinformatics are:

• Transcriptome and proteome analysis
• Gene ranking and biomarker discovery
• Signal identification and FDR
• Graphical models and biological networks
• Molecular evolution

Additional current interests include the analysis of RNA-Seq data, shrinkage methods and logical probability. Most of our methods are available in freely available software. We have also been involved in organizing a number of conferences.

Please find below a representative selection of our publications. For a complete list of publications and for current preprints see the publications of Korbinian Strimmer and the web pages of the other group members.

Transcriptome and proteome analysis:

Example of a protein mass spectrum (click image for a larger view)
We are interested in statistical bioinformatics approaches to analyze gene expression an proteomics data and and also have developed a platform for computational mass spectrometry:

• MALDIquant: a versatile R package for the analysis of mass spectrometry data.
  S. Gibb and K. Strimmer. 2012.
  Bioinformatics 28: 2270-2271.
  (arXiv:1203.5885)
• Analysis of proteomic data using MALDIquant.
  S. Gibb and K. Strimmer. 2011.
  Proceedings of the 8th International Workshop on Computational Systems Biology, WCSB 2011 (June 6-8, 2011, Zurich, Switzerland), pp. 49-52.
• A general modular framework for gene set enrichment analysis.
  M. Ackermann and K. Strimmer. 2009.
  BMC Bioinformatics 10: 47.
• Partial least squares: a versatile tool for the analysis of high- dimensional genomic data.
  A.-L. Boulesteix and K. Strimmer. 2007.
  Briefings in Bioinformatics. 8: 32-44.
• Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach.
  A.-L. Boulesteix and K. Strimmer. 2005.
  Theor. Biol. Med. Model. 2: 23.
• Identifying periodically expressed transcripts in microarray time series data.
  S. Wichert, K. Fokianos, and K. Strimmer. 2004.
  Bioinformatics 20: 5-20.
  Corrigendum. 2008. Bioinformatics 24: 2274.

Gene ranking and biomarker discovery:

CAR regression models for diabetes data (click image for a larger view)
We are interested biomarker discovery and have recently proposed the CAT and CAR scores for ranking of correlated genes. In addition we introduced the shrinkage t statistic, a regularized t-score useful in high-dimensional data analysis with small samples:

• A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies.
  V. Zuber, A. P. Duarte Silva, and K. Strimmer. 2012.
  BMC Bioinformatics 13: 284.
  (arXiv:1203.3082)
• High-dimensional regression and variable selection using CAR scores.
  V. Zuber and K. Strimmer. 2011.
  Statist. Appl. Genet. Mol. Biol. 10: 34.
  (arXiv:1007.5516)
• Gene ranking and biomarker discovery under correlation.
  V. Zuber and K. Strimmer. 2009.
  Bioinformatics 25: 2700-2707.
  (arXiv:0902.0751)
• Accurate ranking of differentially expressed genes by a distribution- free shrinkage approach.
  R. Opgen-Rhein and K. Strimmer. 2007.
  Statist. Appl. Genet. Mol. Biol. 6: 9.
  (typographic corrections)

Signal identification and FDR:

Local FDR thresholds and natural class boundary (click image for a larger view)
We have developed statistical approaches for detection of signal in high-dimension genomic data and for multiple testing using false discovery rates (FDR):

• B. Klaus and K. Strimmer. 2013. Signal identification for rare and weak features: higher criticism or false discovery rates?
  Biostatistics 14: 129-143.
  (arXiv:1112.2615)
• Learning false discovery rates by fitting sigmoidal threshold functions.
  B. Klaus and K. Strimmer. 2011.
  Journal de la Société Française de Statistique 152: 39-50.
  (arXiv:1104.5414)
• Feature selection in omics prediction problems using cat scores and false non-discovery rate control.
  M. Ahdesmäki and K. Strimmer. 2010.
  Ann. Appl. Stat. 4: 503-519.
  (arXiv:0903.2003)
• A unified approach to false discovery rate estimation.
  K. Strimmer. 2008.
  BMC Bioinformatics 9: 303.
  (typographic corrections)
• fdrtool: a versatile R package for estimating local and tail area- based false discovery rates.
  K. Strimmer. 2008.
  Bioinformatics 24: 1461-1462.

Graphical models and biological networks:

Entropy-based gene association network (click image for a larger view)
In our group we have developed a series of algorithms using graphical models for learning large-scale gene association networks from high-throughput data:

• Introduction to graphical modelling.
  M. Scutari and K. Strimmer. 2011.
  Chapter 11 in: M. P. H. Stumpf, D. J. Balding, and M. Girolami (eds.). Handbook of Statistical Systems Biology. Wiley, Chichester, UK, pp. 237-254.
  (arXiv:1005.1036)
• Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks.
  J. Hausser and K. Strimmer. 2009.
  J. Mach. Learn. Res. 10: 1469-1484.
  (arXiv:0811.3579)
• From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data.
  R. Opgen-Rhein and K. Strimmer. 2007.
  BMC Syst. Biol. 1: 37.
• Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process.
  R. Opgen-Rhein and K. Strimmer. 2007.
  BMC Bioinformatics 8 Suppl. 2: S3.
• Inferring gene dependency networks from genomic longitudinal data: a functional data approach.
  R. Opgen-Rhein and K. Strimmer. 2006.
  REVSTAT 4:53-65.
• Reverse engineering genetic networks using the GeneNet package.
  J. Schäfer, R. Opgen-Rhein, and K. Strimmer. 2006.
  R News 6/5:50-53.
• A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.
  J. Schäfer and K. Strimmer. 2005.
  Statist. Appl. Genet. Mol. Biol. 4: 32.
• An empirical Bayes approach to inferring large-scale gene association networks.
  J. Schäfer and K. Strimmer. 2005.
  Bioinformatics 21: 754-764.

Molecular evolution:

Reversible jump MCMC estimate of population size (click image for a larger view)
One of our first research interests were methods for phylogenetic analysis and population genetics using sequence data:

• Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo.
  R. Opgen-Rhein, L. Fahrmeir, and K. Strimmer. 2005.
  BMC Evol. Biol. 5: 6.
• APE: Analyses of phylogenetics and evolution in R language.
  E. Paradis, J. Claude, and K. Strimmer. 2004.
  Bioinformatics 20: 289-290.
• Inferring confidence sets of possibly misspecified gene trees.
  K. Strimmer and A. Rambaut. 2002.
  Proc. R. Soc. Lond. B 269: 137-142.
• Exploring the demographic history of DNA sequences using the generalized skyline plot.
  K. Strimmer and O. G. Pybus. 2001.
  Mol. Biol. Evol. 18: 2298-2305.
• Likelihood mapping: a simple method to visualize phylogenetic content of a sequence alignment.
  K. Strimmer and A. von Haeseler. 1997.
  Proc. Natl. Acad. Sci. USA 94: 6815-6819.
• Quartet puzzling: a quartet maximum likelihood method for reconstructing tree topologies.
  K. Strimmer and A. von Haeseler. 1996.
  Mol. Biol. Evol. 13: 964-969.
  (publisher PDF is defective - instead use this scan)

Conferences:
Our group was coorganizer of the workshop Complex Stochastic Systems in Biology and Medicine 2004 in Munich and we have hosted the Computational Systems Biology (WCSB 2008) conference in Leipzig. More recently, we helped to organize the life science session at GOCPS 2010.

Last modified:
September 12, 2012