STEM (short for the Short Time-series Expression Miner) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). Data from short time series gene expression experiments poses special challenges. In these experiments, thousands of genes are being profiled at the same time while the number of time points is few. In such cases, a lot of genes will have the same expression pattern just by random chance. Moreover as with any time series experiment, there are usually few full time series repeats from which to gain statistical power. Now, scientists at Creative Proteomics are proud to offer our customers STEM Analysis Service.
The features and advantages of STEM analysis
- STEM uses a method of analysis that make use of the number of genes being large and the number of time points being few to identify statistically significant temporal expression profiles and the genes related to these profiles.
- This method also supports Gene Ontology (GO) enrichment analysis for sets of genes with the same temporal expression pattern providing the means for a statistic allyrigorous biological interpretation of significant temporal expression patterns.
- The integration of STEM with GO is bidirectional. STEM can easily identify and visualize the behavior of genes belonging to a specific GO category, determining which expression profiles were enriched for genes in that category.
- Eventually, STEM also supports the comparation of temporal responses of genes across experimental conditions.
How to place an order:
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As one of the leading omics industry company in the world! Creative Proteomics now is opening to provide STEM analysis service for our customers. With rich experience in the field of bioinformatics, we are willing to provide our customer the most outstanding service! Contact us for all the detailed informations!