ALPF Medical Research

» Expression Matrix

  • 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
  • Ovary Function

    Ovary Function

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