Good day everyone, I’m new at this.
Can someone please tell me how I can set a base threshold in order for me to normalize my set of data against it on MIDAS/MeV? What I’m trying to do is use a housekeeping gene’s expression level in my set of data to normalize the rest. Any pointers? Much appreciated
Workings of MIDAS are beyond the scope of MeV. I have emailed one of the developer's at TIGR about your question. He may be able to provide you with more insights. In the meantime, you can also email the MIDAS support team at: firstname.lastname@example.org
I'm John Braisted, from JCVI, formerly of TIGR, and an MeV developer. I sit across from Wei Liang, the developer of Midas. I think Sarita's suggestion of contacting Wei via email@example.com or firstname.lastname@example.org (either email should work) would be the best option for getting an authoritative answer on this question of using a housekeeping gene as a normalization point. I don't believe Midas has this option. It's been our opinion that relying on a small set of genes that are thought to be fairly constant in terms of expression is often a problem (or dangerous) since one can rarely be sure that they don't change. Usually people make the basic assumption that as many genes are up-regulated as down-regulated (relative to a reference sample) across a large set of genes related to diverse functional categories. This assumption can be invalid if the array is small and highly targeted for a particular gene set (functionally related) or if the genome is very small (e.g. prokaryote) and the response is large and covers many or most genes. The above points are related to two-dye platforms. For single dye data like Affy, perhaps the rank invariant normalization might be an option (perhaps in R's Biocondutor package).
Sorry for posting a second time on this but Wei just informed me that his new tool, Ginkgo, which has most or all of the normalization options within midas but also has a large focus on CGH array data, includes the option to normalize relative a single gene or a set of genes based on annotation keys. Ginkgo can be found here: http://pfgrc.jcvi.org/index.php/bioinformatics/ginkgo.html
email@example.com is the tools help email and Wei Liang, the developer of this tool can be reached there.
Further discussion on this can go through Wei at one of his tool email addresses since this is off of the MeV topic.
Thank you, Sarita, for bumping it to the right direction, and many thanks, John, for replying. I'll check out Ginkgo and give it a go. Will revert to Wei Liang when I next need advice.
Truly appreciate your speedy replies :)