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Bioinformatics and Computational Biology Solutions Using R and Bioconductor
limma: Linear Models for Microarray Data
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Author(s):
G. K. Smyth
Publication date
(Print):
2005
Publisher:
Springer-Verlag
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Exponential Random Graph Models
Author and book information
Book Chapter
Pages
: 397-420
DOI:
10.1007/0-387-29362-0_23
SO-VID:
668f4c65-ee7a-4c9c-8bfe-cb4fb6364154
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Book chapters
pp. 3
Preprocessing Overview
pp. 13
Preprocessing High-density Oligonucleotide Arrays
pp. 33
Quality Assessment of Affymetrix GeneChip Data
pp. 49
Preprocessing Two-Color Spotted Arrays
pp. 71
Cell-Based Assays
pp. 91
SELDI-TOF Mass Spectrometry Protein Data
pp. 113
Meta-data Resources and Tools in Bioconductor
pp. 135
Querying On-line Resources
pp. 147
Interactive Outputs
pp. 161
Visualizing Data
pp. 183
Analysis Overview
pp. 189
Distance Measures in DNA Microarray Data Analysis
pp. 209
Cluster Analysis of Genomic Data
pp. 229
Analysis of Differential Gene Expression Studies
pp. 249
Multiple Testing Procedures: the multtest Package and Applications to Genomics
pp. 273
Machine Learning Concepts and Tools for Statistical Genomics
pp. 293
Ensemble Methods of Computational Inference
pp. 313
Browser-based Affymetrix Analysis and Annotation
pp. 329
Introduction and Motivating Examples
pp. 337
Graphs
pp. 347
Bioconductor Software for Graphs
pp. 369
Case Studies Using Graphs on Biological Data
pp. 397
limma: Linear Models for Microarray Data
pp. 421
Classification with Gene Expression Data
pp. 431
From CEL Files to Annotated Lists of Interesting Genes
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