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Journal ArticleDOI

Microarray data normalization and transformation

John Quackenbush
- 01 Dec 2002 - 
- Vol. 32, Iss: 4, pp 496-501
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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.

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The non-coding RNA BC1 is down-regulated in the hippocampus of Wistar Audiogenic Rat (WAR) strain after audiogenic kindling

TL;DR: Functional results recently obtained in a BC1⁻/⁻ mouse model and the current data are supportive of a potential disruption in signaling pathways, upstream of BC1, associated with the seizure susceptibility of WARs.
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Statistically Significant Differences in Composition of Petroleum Crude Oils Revealed by Volcano Plots Generated from Ultrahigh Resolution Fourier Transform Ion Cyclotron Resonance Mass Spectra

TL;DR: In this article, the authors introduce the volcano plot as a simple, visual identification and statistical ranking of compositional differences between petroleum crude oils, which provides a visual means for identifying statistically significant differences between two populations.
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Tools for Interpreting Large-scale Protein Profiling in Microbiology

TL;DR: Quantitative proteomic analysis of microbial systems generates large datasets that can be difficult and time-consuming to interpret, and many of the data display and gene-clustering tools developed to analyze large transcriptome microarray datasets are also applicable to proteomes.
Book ChapterDOI

Neighborhood co-regularized multi-view spectral clustering of microbiome data

TL;DR: A novel algorithm that is based on neighborhood co-regularization of the clustering hypotheses and that searches for the solution which is consistent across different views is proposed, which outperforms several state-of-the-art clustering algorithms.
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Effects of a DPP4 Inhibitor on Progression of NASH-related HCC and the p62/ Keap1/Nrf2-Pentose Phosphate Pathway in a Mouse Model.

TL;DR: DDP4i may prevent tumor progression through inhibition of metabolic reprogramming in NASH-related HCC in a mouse model and downregulated the pentose phosphate pathway with suppression of the p62/Keap1/Nrf2 pathway.
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

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
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