Question: Clustering Genes Based On Gene Ontology
2
gravatar for jellevandewege
6.4 years ago by
Netherlands
jellevandewege90 wrote:

Hi

we have a dataset consisted of a lot of genes and their respective information (like expression). We would like to make a picture to cluster those genes first on their GO terms (gene function) and then on our expression input that we have. Hence, how can I cluster those genes based on their GO terms? Do you have any suggestion?

We are currently programming in R and we would like a R package to solve the problem. We have looked at topGO but that does not give the information we are looking for. Since only uses a small set of the total number of genes.

thanks in advance for your help

R • 8.5k views
ADD COMMENTlink modified 6.4 years ago by seidel7.1k • written 6.4 years ago by jellevandewege90

Seems to be an update to previous question: Clustering genes based on function

ADD REPLYlink written 6.4 years ago by Neilfws48k
0
gravatar for pld
6.4 years ago by
pld4.8k
United States
pld4.8k wrote:

I think DAVID is the tool that you want: http://david.abcc.ncifcrf.gov/gene2gene.jsp. Not in R, but it is a webservice. This will allow you to cluster genes on function. Then you can use hierarchical clustering on the expression values for the genes in each functional group DAVID generates. Maybe hclust: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/hclust.html.

ADD COMMENTlink written 6.4 years ago by pld4.8k
0
gravatar for seidel
6.4 years ago by
seidel7.1k
United States
seidel7.1k wrote:

Clustering on terms is tricky, because much could depend upon what you mean by "clustering on GO Terms". This has been brought up before: Clustering GO terms? (search for geneontology and cluster), and the package GOSim was mentioned by Aswarren. Clustering on terms could be simple and done by hand after an enrichment analysis followed by expression clustering (i.e. if you're just trying to make a figure that makes sense to someone), or complicated and computational if you're truly looking to cluster data based on some semantic similarity.

ADD COMMENTlink written 6.4 years ago by seidel7.1k
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