Nexus Expression

A new take on gene expression analysis

Nexus Expression™ software allows users to quickly perform gene regulation analysis from RNA-Seq and microarray data. Nexus Expression™ is a solution that is computationally very powerful but simple to use by scientists for answering common research questions. Our design objectives for this package have been the same as our very successful Nexus Copy Number™ software:

  • A platform independent solution. 

    (support for all commercial and custom platforms – RNA-Seq or microarray)

  • Efficient processing on a basic desktop computer.

    (hundreds of samples can be processed in one project)

  • A user-friendly interface designed for end-user researchers not informaticians. 

    (no programming knowledge needed)

Nexus Expression™ provides an intuitive and streamlined approach to convert raw signal values into meaningful information by integrating sample phenotypes (e.g., disease stage, survival, age, sex, etc.) with gene annotations (e.g. GO terms).

Users can effortlessly navigate large datasets and generate results quickly. For example, in a cancer study, differentially regulated genes in the HER2 positive sub-population as compared to HER2 negative can be identified in one click and the affected biological processes can be identified with another click. Using a sophisticated ANOVA engine under the hood, the user can easily compare between subgroups, cluster samples and genes, view annotations and perform gene enrichment with ease.

Nexus Expression™ is platform agnostic so data from multiple commercial and custom platforms can be loaded, processed, and viewed with ease.

Get Started Today

 

System Requirements

32-bit Platforms Supported:
Windows Win2k/WinXP/Win7
Minimum:
1.0 GHz Pentium, 1 GB RAM
Recommended:
2.0 GHz or faster, 2 GB RAM
64-bit Platforms Supported:
Windows/OSX/Linux
Minimum:
2 GB RAM
Recommended:
4 GB RAM
One Click Analysis
Raw Data
Affymetrix, Agilent, GenePix, Illumina, Roche NimbleGen, ImaGene and more
Analysis
Statistical computation of differentially regulated genes (with batch effect correction)
Discovery
Cluster samples and genes to discover expression patterns. Color code samples based on phenotypes.
Simultaneously compare expression profiles of multiple comparisons alongside gene annotations.
Identify and display significant genes in affected biological processes comparing between sample groups.
Generate Kaplan Meier plots to estimate survival of different populations.
Find Gene Ontology terms that are significantly over-represented among a sub-group (eg. downregulated probes in short vs. long term survivors).
Perform integrated Gene Set Enrichment Analysis (GSEA) computation.