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
Review of the literature examining the correlation among DNA microarray technologies
Carole L. Yauk,M. Lynn Berndt +1 more
TL;DR: With improvements in the technology, analysis across platforms yields highly correlated and reproducible results, and several key factors should be controlled in comparing across technologies, and are good microarray practice in general.
Journal ArticleDOI
A novel normalization method for effective removal of systematic variation in microarray data
TL;DR: A novel normalization method that uses a matching algorithm for the distribution peaks of the expression log ratio and the robustness and effectiveness of this method was evaluated using both experimental and simulated data.
Journal ArticleDOI
Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease.
Payam Emami Khoonsari,Anna Häggmark,Maria Lönnberg,Maria Mikus,Lena Kilander,Lars Lannfelt,Jonas Bergquist,Martin Ingelsson,Peter Nilsson,Kim Kultima,Ganna Shevchenko +10 more
TL;DR: After depletion of high abundant proteins, Alzheimer’s disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls and global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization.
Journal ArticleDOI
quantro: a data-driven approach to guide the choice of an appropriate normalization method
TL;DR: The utility of the proposed data-driven global normalization method (quantro) is demonstrated through examples and simulations and it is shown that it has the potential to remove biologically driven variation.
Journal ArticleDOI
Quantitative proteomic analysis of drug-induced changes in mycobacteria.
TL;DR: A new approach for qualitative and quantitative proteomic analysis using capillary liquid chromatography and mass spectrometry to study the protein expression response in mycobacteria following isoniazid treatment suggests a complex interplay of metabolic events leading to cell death.
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