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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ChIP-on-chip protocol for genome-wide analysis of transcription factor binding in Drosophila melanogaster embryos.
TL;DR: This protocol describes a method to detect in vivo associations between proteins and DNA in developing Drosophila embryos that combines formaldehyde crosslinking and immunoprecipitation of protein-bound sequences with genome-wide analysis using microarrays.
Journal ArticleDOI
Loss of TSLC1 Causes Male Infertility Due to a Defect at the Spermatid Stage of Spermatogenesis
Louise van der Weyden,Mark J. Arends,Oriane E. Chausiaux,Peter J.I. Ellis,Ulrike C. Lange,M. Azim Surani,Nabeel A. Affara,Yoshinori Murakami,David J. Adams,Allan Bradley +9 more
TL;DR: expression profiling of the testes revealed that Tslc1 null mice showed increases in the expression levels of genes involved in apoptosis, adhesion, and the cytoskeleton, and data show that TSlc1 is essential for normal spermatogenesis in mice.
Journal ArticleDOI
Machine learning in bioinformatics: a brief survey and recommendations for practitioners.
TL;DR: The aim of this paper is to give an account of issues affecting the application of machine learning tools, focusing primarily on general aspects of feature and model parameter selection, rather than any single specific algorithm.
Journal ArticleDOI
Expression profiling of estrogen-responsive genes in breast cancer cells treated with alkylphenols, chlorinated phenols, parabens, or bis- and benzoylphenols for evaluation of estrogenic activity
TL;DR: The results indicate that the variations in chemicals and their biological effects can be monitored by the appropriate grouping of estrogen-responsive genes, and that the genes related to transcription showed the highest degree of variation for the chemicals with relatively high levels of estrogenic activity.
Journal ArticleDOI
Listeria monocytogenes σB Modulates PrfA-Mediated Virulence Factor Expression
TL;DR: Genome-wide transcript profiling and qRT-PCR showed that in the presence of active PrfA (PrfA*), σB is responsible for reduced expression of the Prf a regulon, highlighting the functional importance of regulatory interactions between Prf A and ρB.
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