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The widespread applications of various "omics" technologies in biomedical research together with the emergence of public data repositories have resulted in a plethora of datasets generated for any given physiological state or disease condition. Properly integrating these datasets with similar basic hypotheses can help improve statistical power, reduce study bias, and improve overall biological understanding. INMEX is designed to assist researchers in conducting two common types of such analyses - meta-analysis of multiple gene expression datasets ( meta-analysis) or joint analysis of a gene expression dataset and a metabolomic dataset (integrative analysis), that have been collected under the same or comparable biological conditions.
  • Built-in support for common gene IDs and 47 popular microarray platforms;
  • Built-in support for common metabolite names and IDs from major compound databases;
  • Intuitive interface for processing and annotating individual datasets;
  • Support for well-established meta-analysis methods based on p values, effect sizes, rank products, vote counts, or direct merging;
  • Detailed result tables with summary statistics and gene-wise expression visualization;
  • Heatmap clustering and visualizing the expression profiles for selected gene list;
  • Gene ontology (GO) and KEGG pathway analysis and visualization.

News & Updates

  • Improved box plot generation to prevent errors (07/23/2015);
  • Interface enhancement for data upload (07/15/2015);
  • Added support for Affymetrix Mouse Gene 1.0 ST Array (06/21/2015);
  • Added support for Affymetrix GeneChip Mouse Genome 430 2.0 (06/20/2015);
  • Updated FAQs (05/13/2015);
  • Added more options for data normalization procedure (05/12/2015);
Please Cite
J Xia, CD Fjell, ML Mayer, OM Pena, DS Wishart, REW Hancock (2013) INMEX -- a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res, 41, W63-70. (PDF)