- Analysis of gene expression data matrices
- Analysis of variance
- Assessing the quality of the labelled extract
- Averaging replicate data
- Bayesian or modelbased clustering and fuzzy clustering
- C - 2
- Chens ratio statistics
- Class discovery
- Classification algorithms and class prediction
- Click Click and Expander
- Cluster scoring and validation
- Clustering
- Clustering genes and samples applications of clustering
- Clustering in discretised space
- Condition
- Contents
- Crosshybridisation
- CyberT
- Data management
- Dealing with missing values
- Definition of the problem
- Downstream from expression profile analysis
- Dual labellingdye swapping
- Exogenous spikedin controls
- Experimental design
- Experimental design strategies
- Experimental objectives and features of microarray data
- Filtering low intensity data
- Gene expression matrices
- Gene shaving
- General principles of experimental design
- Global vs local normalisation
- Glossary
- Graphbased clustering
- Hierarchical divisive clustering
- Hybridisation scanning and quality control
- Identification of differentially expressed genes
- Identification of regulatory signals
- Image analysis
- Info
- Intensitydependent estimation of differential expression
- Introduction - 2
- Itnearest neighbour method
- J35m0406
- Linear discriminants
- Linear regression
- Longterm considerations
- Mean log centring
- N
- Neural networks decision trees and applications of classification
- Nl
- Nonhierarchical clustering Xmeans
- Normalisation
- Obtaining the appropriate sample
- Oligonucleotides vs PCR products
- Partially supervised analysis
- Preface
- Preparation of the labelled extract
- Recordkeeping
- Reducing the number of variables
- Reference samples
- References - 2
- Relationship between clustering and PCA
- Replicate filtering
- Replicate guide and control features
- Replicates and repeated measurements
- Representation of expression data as vector space sample space and gene space
- Representation of gene expression data by graphs networks
- Selforganising maps and trees
- Setting floors and ceilings
- Small amounts of sample RNA amplification and pooling
- Standardisation
- Support vector machines
- The central dogma of molecular biology
- Time courses vs independent data points
- Total intensity normalisation
- Tutj
- Types of clustering
- Use of replicate data
- Validation of results
- Visualisation
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