I'm a mathematician trying to test some things on gene expression data, and I'm thus skimming over various articles such as Sotiriou et. al. to understand what is typically done with such data sets. Several things confuse me; in particular, a paragraph in Sotiriou et. al. reads:
"Clinical parameters such as ER status, [...] affect the behavior of breast cancers. We asked whether these clinical/pathologic characteristics were associated with differential gene expression. Parametric t tests identified 606 probe elements of 7,650 elements represented in our array that could segregate ER+ and ER- breast tumors (P < 0.001)."
As segregation of ER+/- based on gene expressions is one of several things I'm interested in attempting to achieve through novel methods, I have been trying to understand what precisely is meant with the above paragrah. To recap the article, there are 99 patients with 7,650 probe expression values, and one ER+/- value each. The article sets out to determine which of those 7,650 probes successfully segregate the dataset into ER+ and ER-.
I've run the above paragraph by a nearby statistician, and he could not for the life of him figure out what was done, and had not even heard of such a thing as a "parametric t test". This leads me to suspect that the term is specific to biology, so I ask: what is meant? It is also unclear to me (and him) what the P-value means in this context.
I hope the scope of this question isn't too broad. Of course I want to avoid asking "explain this article to me, the outsider, please"; I do believe the paragraph above is relatively self-contained in the context of gene expression.