Welcome to week four of Statistics for Genomic Data Science, we're in the home stretch now. So last week, we talked a lot about statistical significance and multiple testing. This week, we're going to talk about what you do after you get a list of genes or snips or other features that you find interesting, how do you sort of summarize them and communicate them. We're going to be talking a little bit about things like Gene Set Enrichment Analysis. Basically ways that you combine all of the statistically significant results in a study with information that we have about what those genes or snips do to try to integrate them in a way that will give you something that you can communicate biologically about the bigger picture process of what's happening when you do a statistical analysis on genomic data. We're also going to touch briefly on other experimental designs that are a little bit more complicated. For example, EQTL studies where you combine multiple different genomic measurements. For example, measurements about snips or DNA information with measurements about RNA or gene expression information, and try to combine those to identify places where snipped variation associates with gene expression variation. So there are a whole bunch of more complicated genomic experimental designs. We're going to talk about just a few of them and start to give you a sneak preview of how you can do integration of multiple data sets in this last week of the class. Good luck with the last week.
