The Analysis of Gene Expression Data Methods and Software

Thedevelopmentoftechnologiesforhigh–throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs. This technological transformation is generating an i...

Full description

Bibliographic Details
Other Authors: Parmigiani, Giovanni (Editor), Garett, Elizabeth S. (Editor), Irizarry, Rafael A. (Editor), Zeger, Scott L. (Editor)
Format: eBook
Language:English
Published: New York, NY Springer New York 2003, 2003
Edition:1st ed. 2003
Series:Statistics for Biology and Health
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • The Analysis of Gene Expression Data: An Overview of Methods and Software
  • Visualization and Annotation of Genomic Experiments
  • Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data
  • An R Package for Analyses of Affymetrix Oligonucleotide Arrays
  • DNA-Chip Analyzer (dChip)
  • Expression Profiler
  • An S-PLUS Library for the Analysis and Visualization of Differential Expression
  • Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data
  • Snomad: Biologist-Friendly Web Tools for the Standardization and NOrmalization of Microarray Data
  • Microarray Analysis Using the MicroArray Explorer
  • Parametric Empirical Bayes Methods for Microarrays
  • SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays
  • Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals
  • MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments
  • GeneClust
  • POE: Statistical Methods for Qualitative Analysis of Gene Expression
  • Bayesian Decomposition
  • Bayesian Clustering of Gene Expression Dynamics
  • Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data