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
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Functional genomic methodologies.
TL;DR: The advantages as well as some of the potential challenges of applying high throughput functional genomics technologies toward a better understanding of brain tissues and diseases using animal models aswell as human postmortem tissues are outlined.
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Transcriptome Analysis on Monocytes from Patients with Neovascular Age-Related Macular Degeneration.
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Introducing insoluble wheat bran as a gut microbiota niche in an in vitro dynamic gut model stimulates propionate and butyrate production and induces colon region specific shifts in the luminal and mucosal microbial community
Kim De Paepe,Joran Verspreet,Kristin Verbeke,Jeroen Raes,Jeroen Raes,Christophe M. Courtin,Tom Van de Wiele +6 more
TL;DR: The long-term effects of insoluble wheat bran supplementation on the composition and functionality of the gut microbial community derived from six individuals were explored in the Dietary Particle-Mucosal-Simulator of the human gut ecosystem in vitro model.
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Tumor-derived Tenascin-C Promotes the Epithelial–Mesenchymal Transition in Colorectal Cancer Cells
Yusuke Takahashi,Genta Sawada,Genta Sawada,Junji Kurashige,Tae Matsumura,Tae Matsumura,Ryutaro Uchi,Hiroki Ueo,Masahisa Ishibashi,Yuki Takano,Sayuri Akiyoshi,Takeshi Iwaya,Hidetoshi Eguchi,Tomoya Sudo,Keishi Sugimachi,Hirofumi Yamamoto,Yuichiro Doki,Masaki Mori,Koshi Mimori +18 more
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Classification between normal and tumor tissues based on the pair-wise gene expression ratio
TL;DR: In the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow, the results indicated.
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