In genomics it's often the case that you don't
want to fit just one regression model.
You want to fit many regression models, so
I'm going to show you a little bit about how to do that.
So I'm going to, again, set everything up just like I like it in terms of plotting,
and then I'm going to load the data set and the packages that we need.
So in this case we need these packages, in particular the Limma.
And edge packages are two packages that we'll be talking about a lot
during this lecture.
So, now I'm going to load the bottom lay data,
which is a data set that compares two different mouse trains.
So we're going to talk a little bit about how you fit those regression models, but
the first thing that I'm going to do is I'm going to basically remove the lowly
expressed genes, just like I have in the past.
I'm going to take the log transform and remove
those that aren't above a particular threshold, so I'm left with 1,000 genes.
So I want to fit 1,000 regression models.
So the first thing that you can do is you could fit this by
basically using the standard default lmfit function in base R.
So the thing I need to do is I need to build a model matrix.
It's going to be the same one for every case.
So this is a common example in genomics where you have
a common design matrix that's the same for every single gene,
basically because you're going to fit the exact same model for every single gene.
And so then you can fit all of those models with the lm.fit function and
you can just pass it the model that you just created, and
the expression set the expression data set that you have.
So the result is a set of coefficients and residuals and
effects from all of those regression models.
And so if I look at the coefficients from the first model fit,
you can actually see that the coefficients matrix is actually very big,
it's got many different models that fit, but
if you look at the first one, and then you compare that to
fitting the linear model to each data set individually.
