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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Global studies of cell type-specific gene expression in plants
TL;DR: The spatial dissection of the transcriptome has yielded insights into the localized mediators of hormone inputs and promises to provide detail on cell-specific effects of microRNAs.
Journal ArticleDOI
A microarray-based comparative analysis of gene expression profiles during grain development in transgenic and wild type wheat.
TL;DR: In this article, the authors compared the gene expression profile in developing seeds of wild type wheat and wheat transformed for endosperm-specific expression of an Aspergillus fumigatus phytase.
Journal ArticleDOI
Applications of DNA tiling arrays to experimental genome annotation and regulatory pathway discovery.
TL;DR: Tiling arrays are expected to become instrumental for the genome-wide identification and characterization of functional elements and combined with computational methods to relate these data and map the complex interactions of transcriptional regulators can provide insight toward a more comprehensive understanding of fundamental molecular and cellular processes.
Journal ArticleDOI
Application of Toxicogenomics to Toxicology: Basic Concepts in the Analysis of Microarray Data
Richard D. Irwin,Gary A. Boorman,Michael L. Cunningham,Alexandra N. Heinloth,David E. Malarkey,Richard S. Paules +5 more
TL;DR: Toxicogenomics is a powerful new tool that may show gene and protein changes earlier and at treatment levels below the limits of detection of traditional measures of toxicity.
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