Data analysis:

Default analysis  >>  Advanced analysis

     

Silicon Genetics's GeneSpring is a powerful visualization and analysis solution designed for use with genomic expression data.
Advanced analysis is performed using GeneSpring and can be customized on data analysis needs and goals.
It is best done if a side-by-side analysis could be done between the service personnel and the referring scientist.
Following is a brief description of software main features.
Normalization:

The analysis of a dataset usually starts with a raw data normalization procedure.
Sixteen transformations are available for creating powerful and flexible normalization scenarios. Normalization steps can be applied in virtually any order and include operations such as dye swapping experiments and median polishing.


Filtering:

GeneSpring offers visually intuitive filtering tools for both entry-level and advanced users.
These filters allow researchers to exclude particular conditions, set minimum and maximum values and choose specific gene lists to filter.
The advanced filtering window allows you to create complex Boolean expressions to identify genes with a highly specific expression pattern.


Advanced Statistical Tools:

GeneSpring provides tools to ask detailed questions about complex data sets.
These include t-tests, 2-way ANOVA tests and 1-way post-hoc tests for reliably identifying differentially expressed genes.
In addition, GeneSpring's class prediction tools can identify genes capable of discriminating between one or more experimental parameters or sample phenotypes.


Working with gene lists:

Finding genes in common between different genelists can be easyly performed using the Venn diagram tool.
By simply dragging the different genelists of interests in the diagram areas, common genes are automatically highlighted and available to be exported.
Annotation of genelists of interests using online databases such as GenBank, LocusLink or Unigene can be obtained using the embedded GeneSpider tool.


Clustering:

Clustering techniques may be used to control experimental reproducibility of the replicate experimental condition or to group genes accordingly to their expression pattern.
A cluster of genes with similar expression pattern may share a common biological function or a binding site for an active transcription factor.
Researchers can use one or a combination of clustering options to characterize their data: gene trees (hierarchical clustering), experiment trees, self-organizing maps, k-means, Principal Components Analysis (PCA) and QT clustering.


Pathways:

With the pathway viewer, genes and their expression patterns can be visually characterized based on their location within a cellular pathway.
Users can design their own pathway diagrams or directly import publicly available pathway maps.
General pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) are supported, together with organism-specific pathways from KEGG.

  
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