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NetworkAnalyst is designed to support integrative analysis of gene expression data through statistical, visual and network-based approaches:

Data inputs: one or more gene/protein lists with optional fold changes; one or more gene expression tables from microarray or RNAseq experiments. Network currently supports 13 species (eight model organisms & five common species).

Expression analysis & meta-analysis: support limma, edgeR and DESeq2. The interface allows paired comparisons, time series, common reference, as well as two-factor nested comparisons; for meta-analysis - p values, fold changes, effect sizes, vote counts, and direct merge.

Network creation & customization: support protein-protein interactions, TF-gene interactions, miRNA-gene interactions, protein-drug interactions and protein-chemical interactions; multiple functions for network refinement;

Network visual analytics: interactive visual exploration - zooming, searching, highlighting, point-and-click, drag-and-drop; network customization - six layout algorithms, background color, edge size/shape, node size/color/visibility; in situ functional enrichment analysis (GO, pathways, etc.); network editing - node deletion, module extraction, and image exporting; topology analysis - hubs, shortest paths analysis, as well as three module detection algorithms.

Other visual analytics: interactive heatmaps, clustering (PCA & t-SNE), Venn diagram and chord diagrams for pattern discovery & complex comparisons.

News & Updates

  • Fixed the issue with SIF file generation (01/16/2017);
  • Fixed issue with PPI mapping for fruitfly (01/10/2017);
  • Added support for Venn diagram visual analytics (12/9/2016);
  • Added support for TF-gene and protein-chemical interactions (12/8/2016);
  • Added support for protein-drug interactions (12/5/2016);
  • Both Network and Heatmap now support Retina display (11/20/2016);
  • Added support for miRNA-gene interactions (11/15/2016);
  • Added support for t-SNE clustering. (11/10/2016);
  • NetworkAnalyst now supports limma, edgeR or DEseq2 for RNAseq data analysis. (10/28/2016);
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