Journal ArticleDOI
Microarray data normalization and transformation
Reads0
Chats0
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
More filters
Journal ArticleDOI
Molecular processes during fat cell development revealed by gene expression profiling and functional annotation
Hubert Hackl,Thomas R Burkard,Thomas R Burkard,Alexander Sturn,Renee Rubio,Alexander Schleiffer,Sun Tian,John Quackenbush,Frank Eisenhaber,Zlatko Trajanoski +9 more
TL;DR: Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.
Journal ArticleDOI
Using GenePattern for gene expression analysis.
TL;DR: This unit demonstrates how to prepare and analyze microarray data in GenePattern, a freely available software package that addresses the issue of difficult to understand and use tools for genomic research in a comprehensive user‐friendly environment.
Journal ArticleDOI
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment
TL;DR: It was deduced that the ranges of inconsistency were caused by experimental errors or additive noise in the data; therefore, excluding the data corresponding to those marginal ranges will prevent misleading analytical conclusions.
Journal ArticleDOI
Mapping cellular transcriptosomes in autopsied Alzheimer's disease subjects and relevant animal models.
TL;DR: A review of recent advances in studies of Alzheimer's disease progression and the use of animal models in gene expression studies of AD progression can be found in this article, where the major finding from these studies is that AD progression and pathogenesis involve multiple cellular pathways, which suggests that AD is a complex and heterogeneous disease.
Journal ArticleDOI
Increased Adherence and Expression of Virulence Genes in a Lineage of Escherichia coli O157:H7 Commonly Associated with Human Infections
Galeb Abu-Ali,Lindsey Ouellette,Scott T. Henderson,David W. Lacher,James T. Riordan,Thomas S. Whittam,Shannon D. Manning +6 more
TL;DR: These findings demonstrate that the EHEC O157:H7 lineage associated with HUS expresses higher levels of virulence genes and has an enhanced ability to attach to epithelial cells relative to another common lineage.
References
More filters
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