Journal ArticleDOI
Microarray data normalization and transformation
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TLDR
This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.Abstract:
Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.read more
Citations
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Finding biomarker signatures in pooled sample designs: a simulation framework for methodological comparisons.
TL;DR: A simulation framework is proposed to explicitly investigate the parameters of patterns, experimental design, noise, and choice of method in order to find out which effects on classification performance are to be expected and a clear increase of prediction error with pool size is shown.
Journal ArticleDOI
Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool
Haiyuan Yu,Katherine T. Nguyen,Thomas Royce,Jiang Qian,Kenneth Nelson,Michael Snyder,Mark Gerstein +6 more
TL;DR: An automated web tool is developed—COP (COrrelations by Positional artifacts) to detect these artifacts in microarray experiments, which find that genes that are close on the microarray chips tend to have higher correlations between their expression profiles.
Journal ArticleDOI
Seeing the unseen: Microarray-based gene expression profiling in vision.
TL;DR: Strategies for designing microarray experiments for expression profiling, image processing including normalization, and data analysis including statistics, clustering, and pathway construction are reviewed.
Patent
Non-invasive determination of methylome of tumor from plasma
TL;DR: In this article, the authors used tissue-specific alleles to identify DNA from the fetus/tumor when the sample has a mixture of DNA and determined the methylation profile by determining a size parameter of a size distribution of DNA fragments.
Journal ArticleDOI
Sigma Factor Regulated Cellular Response in a Non-solvent Producing Clostridium beijerinckii Degenerated Strain: A Comparative Transcriptome Analysis.
TL;DR: Findings indicated that morphological and physiological changes in the degenerated Clostridium strain may be related to altered expression of sigma factors, providing valuable targets for strain development of Clastridium species.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Data Reduction and Error Analysis for the Physical Sciences
TL;DR: In this paper, Monte Carlo techniques are used to fit dependent and independent variables least squares fit to a polynomial least-squares fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI
Data Reduction and Error Analysis for the Physical Sciences.
TL;DR: Numerical methods matrices graphs and tables histograms and graphs computer routines in Pascal and Monte Carlo techniques dependent and independent variables least-squares fit to a polynomial least-square fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI
Robust Locally Weighted Regression and Smoothing Scatterplots
TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
Book
Regression Analysis by Example
Samprit Chatterjee,B. Price +1 more
TL;DR: Simple linear regression Multiple linear regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References as discussed by the authors