Archive
Software for local false discovery rate estimation
LFDR-MLE is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-group parametric mixture model of test statistics.
Postdoctoral training in Bayesian genomics
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://www.statomics.com) to marshal strengths of robust Bayesian, empirical Bayes, and frequentist frameworks. The lab seeks a postdoctoral fellow who will collaboratively develop and apply novel methods of Bayesian inference to overcome current challenges in learning from genome-wide association data, high-dimensional gene expression data, and other data related to genomics.
Experience in computationally intensive data analysis is essential, as is the ability to quickly design and code reliable software implementing Markov chain Monte Carlo algorithms. Strong initiative, excellent communication skills, and reception of a PhD or equivalent doctorate in statistical genetics, statistics, bioinformatics, computer science, mathematics, physics, any field of engineering, or an equally quantitative field within four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: working knowledge of statistical genetics or genomics; familiarly with R, S-PLUS, Mathematica, C, Fortran, and/or LaTeX; experience in a UNIX or Linux environment.
To apply, send a PDF CV that has contact information of three references to dbickel@uottawa.ca, with “Bayes Postdoc” and the year of your graduation or anticipated graduation in the subject field of the message. In the message body, concisely present evidence that you meet each requirement for the position and describe your most significant papers and software packages with summaries of your contributions to them. All applicants are thanked in advance; only those selected for further consideration will receive a response.
Meeting: information theory & statistics
The Fourth Workshop on Information Theoretic Methods in Science and Engineering
7-10 August 2011, Helsinki, Finland
All-scale FDR estimation
D. R. Bickel, “Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions,” Technical Report, Ottawa Institute of Systems Biology, arXiv:1106.4490 (2011). Full preprint
To address multiple comparison problems in small-to-high-dimensional biology, this paper introduces novel estimators of the local false discovery rate (LFDR), reports their main properties, and illustrates their use with proteomics data. Unlike previous LFDR estimators, the new estimators have all of the following advantages:
- proven asymptotic conservatism;
- simplicity of calculation without the tuning of smoothing parameters;
- no strong parametric assumptions;
- applicability to very small numbers of hypotheses as well as to very large numbers of hypotheses.
Observed confidence levels for microarrays, etc.
D. R. Bickel, “Estimating the null distribution to adjust observed confidence levels for genome-scale screening,” Biometrics 67, 363-370 (2011). Abstract and article | French abstract | Supplementary material | Simple explanation
This paper describes the first application of observed confidence levels to data of high-dimensional biology. The proposed method for multiple comparisons can take advantage of the estimated null distribution without any prior distribution. The new method is applied to microarray data to illustrate its advantages.
Unknown Bayes factor approximation
D. R. Bickel, “Measuring support for a hypothesis about a random parameter without estimating its unknown prior,” Technical Report, Ottawa Institute of Systems Biology, arXiv:1101.0305 (2011). Full preprint
Small-scale inference
D. R. Bickel, “Small-scale inference: Empirical Bayes and confidence methods for as few as a single comparison,” Technical Report, Ottawa Institute of Systems Biology, arXiv:1104.0341 (2011). Full preprint
Parametric empirical Bayes methods of estimating the local false discovery rate by maximum likelihood apply not only to the multiple comparison 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.
Local false discovery rate
Efron clearly explains important recent advances in estimating the local false discovery rate. He also contrasts the methodology with more conventional multiple comparison procedures.
Postdoctoral training in statistical inference
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://www.statomics.com) to marshal strengths of the frequentist, empirical Bayes, and Bayesian frameworks. The Statomics Lab seeks a postdoctoral fellow who will collaboratively develop and apply novel methods of statistical inference to overcome current challenges in learning from genome-wide association data, neurodegenerative lipidomics data, and other data related to genomics.
Experience in computationally intensive data analysis is essential, as is the ability to quickly develop reliable software to implement the statistical algorithms developed. Strong initiative, excellent communication skills, and reception of a PhD or equivalent doctorate in statistical genetics, statistics, bioinformatics, computer science, mathematics, physics, any field of engineering, or an equally quantitative field within four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: working knowledge of statistical genetics; familiarly with R, S-PLUS, Mathematica, C, Fortran, and/or LaTeX; experience in a UNIX or Linux environment.
To apply, send a PDF CV that has contact information of three references to dbickel@uottawa.ca, with “Statistics Postdoctoral Fellowship” and the year of your graduation or anticipated graduation in the subject field of the message. In the message body, concisely present evidence that you meet each requirement for the position and describe your most significant papers and software packages with summaries of how you contributed to them. All applicants are thanked in advance; only those selected for further consideration will receive a response.
Confidence intervals for semi-parametric empirical Bayes
D. R. Bickel, “Large-scale interval and point estimates from an empirical Bayes extension of confidence posteriors,” Technical Report, Ottawa Institute of Systems Biology, arXiv:1012.6033 (2010). Full preprint
To address multiple comparison problems in high-dimensional biology, this paper introduces shrunken point estimates for feature prioritization and shrunken confidence intervals to indicate the uncertainty of the point estimates. The new point and interval estimates are applied to gene expression data and are found to be conservative by simulation, as expected from limiting cases. Unlike the parametric empirical Bayes estimates, the new estimates are compatible with the semi-parametric approach to local false discovery rate estimation that has been extensively developed and applied over the last decade. This is carried out by replacing strong parametric assumptions with the confidence posterior theory of papers in the presses of Biometrics and Communications in Statistics — Theory and Methods.
David Bickel, statistician 
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