scispace - formally typeset
Journal ArticleDOI

Microarray data normalization and transformation

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

Content maybe subject to copyright    Report

Citations
More filters
Book ChapterDOI

Methods for genome-wide analysis of gene expression changes in polyploids.

TL;DR: An amplified fragment length polymorphism (AFLP)--complementary DNA (cDNA) display method that allows the discrimination of homoeologous loci based on restriction polymorphisms between the progenitors and microarray analyses that can be used to compare gene expression differences between the allopolyploids and respective progenitor using appropriate experimental design and statistical analysis are described.
Journal ArticleDOI

Urban resilience and livability performance of European smart cities: A novel machine learning approach

TL;DR: In this paper , a two-stage data-driven framework combining a multivariate metric-distance analysis with machine learning (ML) techniques for resilience and livability assessment of smart cities is presented.
Journal ArticleDOI

Comparisons of protein profiles of beech bark disease resistant and susceptible American beech (Fagus grandifolia).

TL;DR: Early identification of resistant trees (prior to the full disease development in an area) will allow forest management through the removal of susceptible trees and their root-sprouts prior to the onset of disease, allowing management and mitigation of costs, economic impact, and impacts on ecological systems and services.
Journal ArticleDOI

Integrin α3 is involved in non-enveloped hepatitis E virus infection

TL;DR: It is suggested that integrin α3 is a key molecule for cellular attachment and entry of neHEV, as well as an essential host factor for HEV infection.
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

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
Related Papers (5)