Differential Expression

By using the R statistical language and microarray packages such as Bioconductor, GeneSifter provides powerful statistics through an intuitive web interface. Comparison statistics can be used to help identify differentially expressed genes and cluster analysis can be used to identify patterns of gene expression and to segregate a subset of genes based on these patterns.

Web based R statistics
Cluster analysis
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Comparison statistics: Comparison tests require replicates and use the variability within the replicates to assign a confidence level as to whether the gene is differentially expressed. Statistical methods available in GeneSifter include:
  • t-test
  • Welch's t-test
  • ANOVA
  • Wilcoxon Rank Sum
  • Kruskal-Wallis
  • Permutation t-test
Correction for multiple testing: Methods for adjusting the p-value from a comparison test based on the number of tests performed. These adjustments help to reduce the number of false positives in an experiment. Methods available include:
  • Bonferonni (FWER)
  • Holm (FWER)
  • W & Y MaxT (FWER)
  • Benjamini and Hochberg (FDR)
Cluster Analysis: - Cluster analysis can be used to identify patterns of gene expression within large datasets and to segregate those genes based on these patterns.
  • Visualization - Several methods are available to help identify patterns in a large data sets
     
    • PCA
    • Hierarchical clustering of samples
    • Hierarchical clustering of genes
       
  • Partitioning - K-means and k-medoids methods are typically used for partitioning, which can be used to separate data into discrete groups or clusters. Methods include:
     
    • PAM
    • CLARA
       
  • Cluster validation - Silhouettes can be used to validate clustering for partitioning methods.

Biological Significance

By combining the identification of broad biological themes with the ability to focus on a particular gene, GeneSifter allows users to rapidly characterize the biology involved in a particular experiment, and to identify particular genes of interest from a list of potential targets.

One Click Gene Summary
Identify Biological Themes
   
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Ontology Reports
Search By Function
   
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Gene Annotation: GeneSifter brings together or links out to more than a dozen gene databases including GenBank, UniGene, LocusLink, The Gene Ontology Consortium, Affymetrix NetAffx, Homologene and KEGG.

Gene Summaries: With the One Click Gene Summary™, information from more than a dozen annotation sources can be used to identify targets for further study, or to prioritize the individual genes on the list based on biological function.

Ontology and Pathway reports: Ontology, Pathway and z-score reports automatically summarize the biological significance of a gene list. By examining the gene list as a whole, GeneSifter users can identify broad biological themes associated with a gene list.

Search by Ontology or Pathway: GeneSifter allows users to rapidly focus in on a defined set of genes based on biological function.

 

For more information, see the GeneSifter Workflow page.