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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Journal ArticleDOI
Gene expression profiling and its application in studies of haematological malignancy
TL;DR: Mechanistically, several promising new targets have been identified, and tantalizing insights have been gained into predicting disease outcome, and more compatible data analysis approaches are needed before the promising classifiers that have been developed can be adopted as diagnostic tools.
Journal ArticleDOI
Different-batch metabolome analysis of Saccharomyces cerevisiae based on gas chromatography/mass spectrometry
TL;DR: This study compared metabolomics methods for Saccharomyces cerevisiae in terms of reproducibility to optimize the analytical workflow, particularly quenching and data processing and showed that reproducible data could be obtained with high signal to noise ratio.
Journal ArticleDOI
Gel-Based Microchips: History and Prospects
Alexander Kolchinsky,Dmitry A. Gryadunov,Yu. P. Lysov,V. M. Mikhailovich,Tatyana V. Nasedkina,A. Yu. Turygin,A. Yu. Rubina,V. E. Barsky,Alexander S. Zasedatelev +8 more
TL;DR: The history of formation and development of the microchip technology and its role in the human genome project in Russia is described and the oligonucleotide microchips are described, a cheap and reliable diagnostic tool designed for mass application.
Journal ArticleDOI
Classification of genomic data: some aspects of feature selection.
TL;DR: It is shown that it is possible to select different subset of genes for classification due to the correlation of genes but their interpretation ought to be cautiously made, as sets of the selected variables depend on the objective function of the classifier.
Book ChapterDOI
Extrapolating traditional DNA microarray statistics to tiling and protein microarray technologies.
Thomas Royce,Joel Rozowsky,Nicholas M. Luscombe,Olof Emanuelsson,Haiyuan Yu,Xiaowei Zhu,Michael Snyder,Mark Gerstein +7 more
TL;DR: Some of the most widely used statistical techniques for normalizing and scoring traditional microarray data and indicate their potential utility for analyzing the newer protein and tiling microarray experiments are presented.
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