Hello All,
I am working on a project wherein we are comparing two methods.
In one method: we see that majority of genes are showing improvement in correlation between predicted and residual expression in comparison to other method.
For example: let says we have 500 genes and are using elastic net and Bayesian method to model gene expression.
method1: 378/500 shows improvement in correlation whereas method2: 122/500: gives good correlation score.
How can we say significantly that method 1 is better in comparison to method2?
Basically, how can I say that 378 genes out of 500 genes their improvement is significant in comparison to method 2?
Is there any way to elucidate this information?
It's not clear what these methods are doing. Maybe use the mean squared error over all the genes?
Basically both the method produce a gene expression model, we then use the model to predict expression on the remaining datasets and generate a correlation between predicted and actual expression.
Then the MSE would be enough.