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Next steps for analyzing differential gene expression in mussel feet #1774
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I would start out doing single comparison within tissue type. gill - control v hypoxia, |
Hi Grace, Steven beat me to it, but yes - single comparisons will give you the DEG lists you are after. The design may look a bit complicated at first, but really all treatments within a given tissue type can be compared to their respective control treatment at day 0 (the treatment control, naïve mussels that were samples from the wild). The samples labeled control at day 3 act as an laboratory control. With a laboratory control you can determine what the impact of holding the mussels in the lab for 3 days was; does being in the lab mimic the effects of one of the treatments? With this in mind, we have four "treatments" and one true control: Control:
Treatments:
When constructing your colData input for DESEQ (I think you called it tissuesub), I would have have three columns:
You can then filter your count matrix by tissue or treatments before inputting into the DESeqDataSetFromMatrix argument like this:
The order of the contrast_list argument is important as it sets which "treatment" is the control or baseline condition Before doing pairwise comparisons though, I would throw all the data from a single tissue type in an visualize using the PCA or biplots like this:
Looking at the biplot should help identify outliers as well Here is the oyster code that I referenced earlier that you can use as a guide. |
Okay I've been poking around with it and for some reason when I try to create the p object (for pca) I keep getting this error: Error: useNames = NA is defunct. Instead, specify either useNames = TRUE or useNames = FALSE. Do I not have the correct packages installed? What do I need to run this command? |
Link (and/or copy/paste) code you're trying to run? Also, the message tells you what to do. Instead of |
The pca() function used here is from the bioconductor package PCAtools. Try For reference, here is the installed packages that I used in the oyster code. Running the sapply chunk will load/install dependencies after you have installed the bioconductor packages manually with the commented out lines at the top.
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Okay I installed everything, I'm still getting that same error. Also, I should've included this before, but I've tried using the useNames argument (both False and True) and I keep getting this error: Error in pca(assay(dds2vst), metadata = treatmentinfo.F, useNames = TRUE) : So if I include it the code won't run, but if I don't include it the code won't run. R did yell at me when I installed the packages though because the version of R on Raven is not the most current (and therefore isn't compatible with the newest version of Bioconductor), could that be the culprit? |
Possibly try solution here: satijalab/seurat#7501 (comment) For this:
Please post commands and screenshot(s) of warnings/error messages. |
Thank you, I tried that solution unfortunately to no avail. R is stubborn. |
Did you try that second option (saw that, too, but wanted you to try the first possible solution). Sorry, this isn't clear to me:
So, are you saying you are not getting an error message any more? |
Try restarting your R session, try again, and please report back. |
@graceleuchtenberger Sorry - just saw link at top |
Can you add me as collaborator so I can push - and to clarify the current issue is you can create a PCA? |
Steven I should've just added you, check for an invite and let me know. |
@graceleuchtenberger - I've created a separate issue for resolving the package installation issue here: #1790. Please switch to that issue to deal with this specific problem. |
Hi all,
I did a preliminary DGE between foot and gill tissue just to see what I could get, my code is here.
I'm thinking in the treatment info file (the file DEseq uses to categorize samples by treatment group) I'll add another column with day of treatment (either day 0 or 3), and another column with experimental treatment (control, hypoxia, ocean warming, ocean acidification). I'm guessing that DESeq will struggle with having four treatment groups, I can keep looking at Sam Bogan's multifactorial gene expression tutorial on Marineomics, but any initial recommendations? Does it seem like I'm on track?
Thanks!
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