Ok I understand that and I will definitely do that....However, its also important to check the validity of the method when applied to gene expression as the goal is to have a set of clusters where genes belonging to the same cluster share similar function....Is there a dataset that gives me genes that have known functions?
How To Evaluate The A Newly Developed Clustering Algorithm?
Hello,
I developed a new clustering algorithm specific for gene expression for gene function prediction... I'm interested in assessing the validity of my method through some biological datasets. Is there a dataset where it lists genes which have similar functions? I want to check if genes which have similar functions are clustered together....
thanks
• 2,375 views
•
link
updated
by
Istvan Albert
•
written
by
ftp
•
written
by
ftp
1 answer
The way to evaluate the algorithm is to generate random data, cluster it then verify that the data that you have in the clusters do indeed belong there.
Trying to evaluate the quality of clustering from a second, possibly unrelated attribute is not the correct approach.
• 0 views
•
link
written
by
Istvan Albert
Log in to answer this question.
More posts like this
-
Cell-type-specific markers across datasets
written by Chlogst •
-
Annotating cell types via integrating a query dataset with a reference dataset and then cluster
written by Kaz •
-
Experimental/Biological datasets for Gene Regulatory Network Reconstruction
written by Anjali Setiya •
-
Problem in hierarchical clustering
written by Calangoa •
-
What is the best clustring method for gene expression base on logFC
written by Calangoa •
-
Inferring gene function from homologus genes.
written by giuseppe.minardi95 •
-
Not clustered terms in david ontology functional annotation clustering
written by iraia.munoa
-
Clustering and extracting gene IDs with same expression profiles
written by lessismore
-
Method to identify tissue specific genes in plant species?
written by kozhaki.seq
-
Differentially expressed gene clustering
written by seta
If you are clustering on gene expression levels alone, I fail to see why two genes with similar function would cluster. Just because two genes are kinases, they most likely won't have the same expression levels. At least I've never seen evidence for such.
Also, how do you define "similar function"? Do two genes have similar function if they're both kinases? If they're part of the same pathway? If they're both transmembrane? etcetcetc. If you define similar function by pathway, there's plenty of data sets.
Can you direct me to one of the pathway datasets? and yes i define similar function by pathway
MSigDB is probably the most easy to parse. There are pathways from both KEGG and REACTOME.