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
Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips
TL;DR: The results indicate that some probe sets should not be considered as unique measures of transcription, because the individual probes map to more than one transcript dependent upon the biological condition, and highlight the need for care when assessing whether groups of probe sets all measure the same transcript.
Journal ArticleDOI
Methods, algorithms and tools in computational proteomics: A practical point of view
TL;DR: A broad overview of a number of computational tools available for data analysis of MS‐based proteomics data is provided and appropriate literature references are given to detailed description of algorithms.
Journal ArticleDOI
Integration of Carbon and Nitrogen Metabolism with Energy Production Is Crucial to Light Acclimation in the Cyanobacterium Synechocystis
Abhay K. Singh,Thanura R. Elvitigala,Maitrayee Bhattacharyya-Pakrasi,Rajeev Aurora,Bijoy K. Ghosh,Himadri B. Pakrasi +5 more
TL;DR: Time series transcriptome data is utilized to elucidate the global responses of Synechocystis to high light and indicates a comprehensive integrated homeostatic interaction between energy production and energy consumption and the existence of a novel glycosylation pathway.
Book ChapterDOI
Statistical Issues in Microarray Data Analysis
TL;DR: Any microarray experiment consists of several components: carrying out an appropriately designed (replicated) plant experiment; array processing, which includes several steps of data acquisition and normalization; and analysis of expression data to identify differentially expressed genes and overall patterns of expression.
Journal ArticleDOI
Multiple mutations in heterogeneous miltefosine-resistant Leishmania major population as determined by whole genome sequencing.
Adriano C. Coelho,Sébastien Boisvert,Angana Mukherjee,Philippe Leprohon,Jacques Corbeil,Marc Ouellette +5 more
TL;DR: The study demonstrated the polyclonal nature of a resistant population with individual cells with varying susceptibilities and genotypes and highlighted that resistance can be highly heterogeneous at the population level with individual clones derived from this population differing both in terms of genotypes but also susceptibility phenotypes.
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