-
Notifications
You must be signed in to change notification settings - Fork 12
Description
Alright, so I'm restarting much of my pipeline from the start, as I realized a flaw in my previous analysis (I was grouping Day 0 and Day 2 crabs at different temperature treatments, even though Day 0 samples were taken prior to exposure to different temperatures). The plan is as before: use kallisto to create a matrix of counts for each temperature group, then use DESeq2 to perform a differential expression analysis, then get the GO terms and use GO-MWU to perform a gene enrichment analysis.
The good news: This gives me a chance to recalibrate what libraries I want to include in my analysis of DEGs at different temperature treatments! I have 3 possibilities, as follows:
Option 1: Balanced Design
| Temperature | Num of samples |
|---|---|
| Elevated | 3 |
| Ambient | 3 |
| Lowered | 3 |
Option 2: Imbalanced Design
This option disregards a balanced design, and samples all possible infected crab from their respective treatment group
| Temperature | Num of samples |
|---|---|
| Elevated | 4 |
| Ambient | 10 |
| Lowered | 3 |
Option 3: Imbalanced Design - This time, it's imbalanced-er
This option adds some bonus ambient-temperature crab by including Day 0 crab that were part of the ambient and low-temperature treatment groups (and, since they were Day 0, had been held at ambient temperatures). This may be a plus or a minus, but this means that several individuals would be present in multiple temperature treatments (ex: Crab A is counted as an ambient library on Day 0, but a elevated-temperature library on Day 2)
| Temperature | Num of samples |
|---|---|
| Elevated | 4 |
| Ambient | 14 |
| Lowered | 3 |
Any recommendations on the best approach to take here?