Enriched analysis

Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, MA., Paulovich, A., Pomeroy, S.L., Golub, T.IL, Lander, E.S., et al, (2005) Gene Set Enrichment Analysis A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proc Natl Acad Sci USA, 102, 15545-15550. 

In future experiments a greater emphasis should also be placed on the complementary analysis of all the major reaction products in addition to the isotopic enrichment analysis. 

Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102 15545-15550... 

Methods for Functional and Pathway Enrichment Analysis of Selected Gene Lists... 

After preprocessing the different data types, they are ready for data fusion. Two different types of data fusion between metabolome and transcriptome data can be distinguished. Low-level fusion combines raw data of both data types to produce new raw data. In contrast to this high-level fusion, results from independent data analysis are merged for combined interpretation. The latter is the case for often-used tools such as overrepresentation or enrichment analysis. 

After identification of gene-metabolite associations or enrichment analysis, visualization is a second key point. Results from low-level data fusion often yields pairwise correlations, which can be visualized using networks. In the case of high-level data fusion, a combined visualization is used on metabolic pathways (e.g., the well-known pathway maps from KEGG are preferred). We discuss some technical resources for both visualization types. However, much more tools for different kinds of visualization exist and are reviewed elsewhere (29). 

Increasing number of papers using combinations of different Omics approaches are published, whereas transcriptomics and metabolomics are often preferred. Combined analysis of both can be carried in different ways, as shown above. Different software tools have been developed for analysis of each single technology, but solutions for combined analysis are emerging. This is especially true for overrepresentation or enrichment analysis in high-level data fusion. With more powerful computer infrastructure available even low-level data fusion (e.g., correlation analysis) will be conducted. Here computational power will be needed because calculation time increases not linearly with data size but rather quadratic or higher. 

Chagoyen M, Pazos F (2011) MBRole enrichment analysis of metabolomic data. Bioinformatics 27(5) 730-731... 

Oda, Y Nagasu, T Chait, B.T. "Enrichment Analysis of Phosphorylated Proteins as a Tool for Probing the Phosphoproteome, Nature Biotechnol. 19, 379-382 (2001). 

Fig. 6. Enrichment analysis results for clozapine targets against GeneGo Pathway Maps and GeneGo Biological Processes. Horizontal bars indicate the negative log of the p-value from the hypergeometric distribution calculation.

The results of enrichment analysis calculations for possible targets can be accessed directly from the compound report for the uploaded molecule ... 

In the Possible Targets section of report, select the targets identified for the query compound and its metabolites to be included in the enrichment analysis... 

Open then Enrichment Analysis section of the report and review the results for the ontologies of interest... 

The Close neighbors option is useful to expand the number of targets used in enrichment analysis in cases where similarity searching returns only a small number of possible targets, in which case the results of enrichment analyses may not be accurate. 

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