263 research outputs found
The Importance of Reproducible Research in High-Throughput Biology
No full textDecember 12, 2024
Keith Baggerly, PhDProfessor, Department of Bioinformatics and Computational Biology, (retired)https://openworks.mdanderson.org/igct_workshops/1004/thumbnail.jp
Adaptive Importance Sampling on Discrete Markov Chains
No full text: In modeling particle transport through a medium, the path of a particle behaves as a transient Markov Chain. We are interested in characteristics of the particle's movement conditional on its starting state which take the form of a "score" accumulated with each transition. Importance sampling is an essential variance reduction technique in this setting, and we provide an adaptive (iteratively updated) importance sampling algorithm that is proven to converge exponentially to the zero-variance solution. Examples illustrating this phenomenon are provided. Running Title: Adaptive Importance Sampling AMS 1991 subject classifications: 65C05. KEY WORDS: Adaptive procedures, exponential convergence, Monte Carlo, particle transport, zero variance solution. 1 Craig Kollman is a Biostatistician at the National Marrow Donor Program, Minneapolis, MN; Keith Baggerly and Dennis Cox are Assistant Professor and Professor, respectively, in the Statistics Department of Rice University, Houston, TX; a..
Models for the preprocessing of reverse phase protein arrays
No full textReverse-phase protein lysate arrays (RPPA) are becoming important tools for the analysis of proteins in biological systems. RPPAs combine current assays for detecting and measuring proteins with the high-throughput technology of microarrays. Protein level assays have the ability to address questions about signaling pathways and post translational modifications that genomic assays alone cannot answer. The importance of preprocessing microarray data has been shown in a variety of contexts over the years and many of the same issues carry over to RPPAs including spot level correction, quantification, and normalization.
In this thesis, we develop models and tools to improve upon the standard methods for preprocessing RPPA data. In particular, at the spot level, we suggest alternative methods for estimating background signal when the default estimates are compromised. Further, we introduce a multiplicative adjustment at the spot level, modeled with a smoothed surface of the positive control spots, that removes spatial bias better than additive-only models. When mutli-level information is available for the positive controls, a method that builds nested surfaces at the positive control levels further decreases spatial bias.
At the quantification level, we outline a newly developed R-package called SuperCurve. This package uses a model that borrows strength from all samples on an array to estimate both an over all dose-response curve and individuals estimates of relative sample protein expression. SuperCurve is easy to implement and is compatible with the latest version of R.
Finally, we introduce a normalization model called Variable Slope (VS) normalization that corrects for sample loading bias, taking into account the fact that expression estimates are computed separately for each array. Previous normalization models fail to account for this feature, potentially adding more variability to the expression measurements. VS normalization is shown to recover true correlation structure better than standard methods.
As processing methods for RPPA data improve, this technology helps identify proteomic signatures that are unique to subtypes of disease and can eventually be applied to personalized therapy
Incorporating Density Estimation Into Other Exploratory Tools
No full text: Preliminary understanding of a new data set is routinely accomplished with graphical tools, such as those popularized originally by EDA. A number of more recent ideas for multivariate data analysis have emerged and some are available in software packages or shareware such as XGobi. In this talk, we illustrate how many of the point-oriented techniques can be supplemented by incorporating nonparametric density estimates. Examples from the grand tour to parallel coordinates to clustering will be presented. Potential advantages include visual simplicity, recognition of unusual structure, and handling an additional dimension. KEY WORDS: Density Estimation, Exploratory Data Analysis, Grand Tour, Scatter Diagrams, Parallel Coordinates, Averaged Shifted Histogram. 1 Paper presented at the Annual Meetings of the ASA, Orlando, Florida, August 15, 1995. The author would like to thank John D. Salch for assisting in the creation of the video tape and figures in this paper, and Keith Baggerly for..
When is Reproducibility an Ethical Issue? Genomics, Personalized Medicine, and Human Error
No full textNon UBCUnreviewedAuthor affiliation: MD Anderson Cancer CenterFacult
Emergence of stable DNA repeats from random sequences under unequal sister chromatid exchange
No full text- …
