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Posts tagged “KEGG

Enrichment Network

Enrichment is beyond random occurrence within a category. Networks can represent relationships among variables. Enrichment networks display relationships among variables which are over represented compared to random chance.

Next is  a tutorial for making enrichment networks for biological (metabolomic) data in R using the KEGG database.


Making Chemical Similarity Networks

Chemical similarity networks (CSN) can be used to explore multivariate metabolomic data within a biological context. In CSN networks, nodes represent metabolites and edges are formed between metabolite product-to-precursor  pairs or structurally similar chemical species.

Here is an example of a chemical similarity network generated from a GC/TOF metabolomic experiment on serum.

Chemical Similarity Network

This was done following the steps outlined below.

A) Get similarity matrix from pub chem: http://pubchem.ncbi.nlm.nih.gov//score_matrix/score_matrix.cgi

1) paste in CIDs (pubchem ids) in “IDs List”

2) hit submit button on top

3) copy results<–paste below in #2


B) Use Metamapp to generate edge attribute files

1) select chemical and biochemical map option

2) paste in 2 column matrix with CIDs and KEGG ids in field: “Enter CID KEGG Id Pair”

2) paste results from pub chem similarity score in field “Enter Similarity Matrix Data”


C) use KEGG react pairs or network “edge attribute files” to connect metabolites

1) optionally filter connections based on score to select top hits (>75)

2) optionally convert CIDS to metabolite names (need to replace spaces in name with some character, “_”)

3) save as txt or csv file


D) visualize in Cytoscape

1) import table using setting “Network from table (Text/Ms Excel)”

2) select the three columns as 1) source 2) interaction type 3) interaction target


The next thing to do is to

E) annotate node attributes based on statistical test results or biochemical domain knowledge


This is where ExCytR will be very helpful…(to be continued…)