Cancer cells often exhibit a change in number of copies of certain genomic regions when compared to normal cells (Copy Number Alterations: CNAs). Some of these CNAs [...]
On the quest to identify cancer driver genes, it has been observed that driver alterations that affect a pathway tend to be altered in a mutually exclusive manner. [...]
We have published a new section in IntOGen in collaboration with Hautaniemi Lab, where you can do a gene correlation with the gene expression results of TCGA (The [...]
When we created IntOGen we had the motivation to convert it into a discovery tool for cancer researchers and a resource that integrates multidimensional OncoGenomics Data. We [...]
The other day I downloaded the cancer-affected Gene Ontology (GO) terms from IntOGen for up- and down-regulation via it's Biomart interface for a few tissues. Since was only interested in the GO Cell Compartment terms, so I directly added a filter file containing all the GO CC Terms as a filter for the Biomart export. So then... what do you do when you have a list of GO terms? Already if it is only 100 GO terms, it is quite hard to get an idea which are the affected compartments. To understand better you have to identify the more general terms that are affected. Here I explain quickly how I solved this problem and share it with you.
In this series of posts I am showing how expression data can be analyzed using Gitools. In a previous post I explained how to do pathway enrichment [...]
After preparing some tutorials for our software, I thought it would be useful to show how basic analyses on microarray data can be carried out using Gitools. [...]