When multiple factors are affecting gene expression in your project, you can utilize a regression-based method of finding the relationship between expression values and levels of factors. Regression analysis is a way of mathematically sorting out which of the independent variables have an impact on the data we have (i.e. gene expression). It answers the following questions:

●       Which factors matter most and which can we ignore?

●       How do those factors interact with each other?

●       How can we measure the influence of these factors on our data?

In regression analysis, those factors are called variables. There are dependent variables — the main factor that you’re trying to understand or predict. Then, there are independent variables — the factors you suspect have an impact on your dependent variable. In our example, we will select 8 samples from the breast cancer PDX project and see how cancer type (TN and ER+) and mouse type (NOD SCID and Athymic Nude) influence expression. In order to conduct a regression analysis, you gather the data on the variables in question, and then plot all of that information, to learn how the data is gathered, plotted and analyzed, explore the lesson  Regression & Factor Regression Analysis : https://learn.omicslogic.com/Learn/course-5-transcriptomics/lesson/10-t2-regression-and-factor-regression-analysis under transcriptomics course on the OmicsLogic learn portal.