A Bayesian approach to informing decision makers

23 September 2016 Comments off

Undergraduate research project or internship

2 September 2016 Comments off

Acquire a statistical bioinformatics skill set by developing novel scientific software in the frontiers of genomics for high impact on medical science. Learn to analyze genomics data with newly created statistical methods. Make new biostatistics software accessible worldwide by improving the usability and functionality of the Statomics Lab’s data analysis code and by adding documentation. Providing scientists with these reliable biostatistical tools can advance medical research by improving the accuracy of conclusions drawn from genomics and clinical data.

Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the Statomics Lab (http://davidbickel.com) aims to develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with either “research project” or “internship” in the Subject line of the message and with a cover letter in the body of the message. Only those students selected for further consideration will receive a response.

Categories: applications welcome

Statistics & biostatistics graduate studentships

1 September 2016 Comments off

Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://davidbickel.com). The Statomics Lab seeks new graduate students who will conduct original research involving the creation and evaluation of novel statistical tools for application to the analysis of transcriptomics, proteomics, metabolomics, and/or genome-wide-association data.

Each student will work toward an MSc or PhD degree in the Mathematics and Statistics Program at the University of Ottawa. MSc students have the additional option of choosing a Bioinformatics or Biostatistics Specialization. Financial support is available.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

Canadians (by citizenship or permanent residency) are especially encouraged to apply, as are all exceptional students. To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with either “MSc” or “PhD” and any specialization in the Subject line of the message and with a cover letter in the body of the message. Only those selected for further consideration will receive a response.

Categories: applications welcome

Estimates of the local false discovery rate based on prior information: Application to GWAS

1 August 2016 Comments off

Empirical Bayes single-comparison procedure

1 July 2016 Comments off

D. R. Bickel, “Small-scale inference: Empirical Bayes and confidence methods for as few as a single comparison,” International Statistical Review 82, 457-476 (2014). Published version2011 preprint

Parametric empirical Bayes methods of estimating the local false discovery rate by maximum likelihood apply not only to the large-scale settings for which they were developed, but, with a simple modification, also to small numbers of comparisons. In fact, data for a single comparison are sufficient under broad conditions, as seen from applications to measurements of the abundance levels of 20 proteins and from simulation studies with confidence-based inference as the competitor.

Adaptively selecting an empirical Bayes reference class

1 June 2016 Comments off

F. A. Aghababazadeh, M. Alvo, and D. R. Bickel, “Estimating the local false discovery rate via a bootstrap solution to the reference class problem,” Working Paper, University of Ottawa, deposited in uO Research at http://hdl.handle.net/10393/34295 (2016). 2016 preprint

Categories: empirical Bayes, preprints

Empirical Bayes software (R packages)

1 May 2016 Comments off
Categories: empirical Bayes, software