Hello there!
I'm hoping someone can help me set up an accurate model to fit my gene expression data (RNAseq) using limma. My study involves 10 patients. Two samples were collected from each patient (one cancer site and one uninvolved site), for a total of 20 samples.
patient | condition |
---|---|
P01 | uninvolved |
P01 | cancer |
P02 | uninvolved |
P02 | cancer |
P03 | uninvolved |
P03 | cancer |
P04 | uninvolved |
P04 | cancer |
P05 | uninvolved |
P05 | cancer |
P06 | uninvolved |
P06 | cancer |
P07 | uninvolved |
P07 | cancer |
P08 | uninvolved |
P08 | cancer |
P09 | uninvolved |
P09 | cancer |
P10 | uninvolved |
P10 | cancer |
I want to account for the patient effect and ultimately compare genes up/down regulated between cancer and uninvolved groups. I've been looking through limma and edgeR's user manuals, as well as reading through the various posts here. If you have any guidance, please let me know.
Thanks in advance.Xx.