phc240c project @ berkeley
Berkalieva, Asem
Espejo, Edie
Trikannad, Namita
Zhu, Hengcheng
Endometriosis is an estrogen-dependent condition that affects nearly 10% women of reproductive age and is characterized by chronic pain and inflammation. Currently, it can only be fully diagnosed at surgery and has an average latency period of 11 years before it is diagnosed. Lack of research knowledge in this domain is a major roadblock in the development of novel diagnostics and therapeutics for Endometriosis. Profiling the deviations in gene expressions from normal to mild and severe stages of endometriosis is of high value towards understanding the disease and identifying diagnostic and therapeutic targets.
We propose to utilize a publicly available microarray data containing archived endometrial samples from women with different stages of endometriosis and derive gene expression signatures. We will use statistical techniques applicable to the analysis of high dimensional genomic data learnt from and beyond the course. In addition to incorporating the familiar methods listed below, we will research novel methods that we plan to incorporate into our analysis.
Reference paper:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239429/
Supplementary material:
http://press.endocrine.org/doi/suppl/10.1210/EN.2014-1490/suppl_file/en-14-1490.pdf
Genbank Accession:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51981
Geo download:
https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE51981