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
Transformations for cDNA microarray data.
TL;DR: A general model for signal intensity data with multiple error sources is presented and a regional smoothing method is proposed to remove variation in log ratios due to spatial heterogeneity on the microarray surface.
Journal ArticleDOI
A simple array platform for microRNA analysis and its application in mouse tissues
TL;DR: A simple array platform that can detect 553 nonredundant mi RNAs encompassing the entire set of miRNAs for humans and mice is reported, and a new approach of data normalization based on Northern blot analysis of one ubiquitously expressed miRNA is introduced and compared with traditional approaches.
Journal ArticleDOI
Comparison of in vitro and in vivo reference genes for internal standardization of real-time PCR data.
TL;DR: Comparisons to three stably expressed reference genes gave evidence that the spiking of template RNA with in vitro transcribed cRNA is a valuable tool for internal standardization of real-time PCR experiments.
Journal ArticleDOI
Critical appraisal of DNA microarrays in psychiatric genomics.
TL;DR: It is proposed that for some genes, altered expression in the postmortem human brain may have a dual origin: polymorphisms in the candidate genes themselves or upstream genetic-environmental factors that converge to alter their expression level.
universidade de santiago de compostela
TL;DR: In this article, the formulation of totally formaldehydefree adhesives based on tannins obtained from some lignocellulosic forest and/or industrial wastes was considered.
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