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
Computational methods for ribosome profiling data analysis
TL;DR: This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process and pinpoints challenges associated with each step and explains the ways in which they are currently addressed.
Journal ArticleDOI
Microarray expression technology: from start to finish.
TL;DR: This review will give an overview of the various technologies that are available to perform a microarray expression experiment, from the initial planning stages through to the final data analysis.
Journal ArticleDOI
TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources.
Luca Lenzi,Federica Facchin,Francesco Piva,Matteo Giulietti,Maria Chiara Pelleri,Flavia Frabetti,Lorenza Vitale,Raffaella Casadei,Silvia Canaider,Stefania Bortoluzzi,Alessandro Coppe,Gian Antonio Danieli,Giovanni Principato,Sergio Ferrari,Pierluigi Strippoli +14 more
TL;DR: TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources, and is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes.
Journal ArticleDOI
Understanding the behavior of foodborne pathogens in the food chain: New information for risk assessment analysis
TL;DR: The application of quantitative PCR and microarrays for the determination of the gene expression in situ, is a new field of research for food microbiologists and provides new information regarding virulence potential of foodborne pathogens.
Journal ArticleDOI
Molecular immune response of the American lobster (Homarus americanus) to the White Spot Syndrome Virus.
TL;DR: Competing evidence is provided that H. americanus is capable of mounting an immune response specific to infection by different pathogen classes, including the White Spot Syndrome Virus.
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