(Now I have November and December Dwights!)
So…I guess it’s the end of the year now. Welp. Cue the cramming of work into what’s left of the quarter!
November Goals Recap:
- Turned in my milestone paperwork!
- Successfully presented at GSS and WSN
- Half-completed my DNR Proteomics paper
- Revised methods and results
- Outlined discussion
- Made good progerss on integrating many data sources for my analyses
- Finish. this. proteomics. paper.
- Identify sequencing protocol for C. virginica gonad samples
- Prepare C. virgincia samples for sequencing
- Add Disqus to my blog
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The last bit of data!
Micah sent over growth data for the oysters, as well as site rankings for outplant depth and eelgrass extent in the bay.
Outplant elevation, from deepest to most shallow: Case Inlet (never dry), Fidalgo Bay (never dry), Skokomish (exposed at low tide), Port Gamble Bay (exposed at low tide), Willapa Bay (frequently exposed at low tide)
Eelgrass extent in bay, from most to least: Fidalgo Bay (eelgrass dominant), Willapa Bay (eelgrass dominant), Port Gamble Bay (eelgrass common), Case Inlet (eelgrass common), Skokomish (eelgrass limited)
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Yesterday’s meeting notes
In the third installment of our “what does all of this actually mean” meetings, Micah, Alex, Emma, Brent, Laura, Steven and I discussed the progress we’ve made integrating all of our data into one cohesive story.
- Dissolved oxygen measurements
- FB most eelgrass dominated, higher pH, could have daily super saturation (DO > 12)
- Need to do literature survey to verify measurements are “real”
- Padilla Bay: DO ~ 19.3 max for sensors that never come out of the water
- Should clip DO, pH and salinity data
- Conservative one hour/one foot clipping
- Use Union for SK tidal data
- Just use bare for all sites
- Correcting values to the right mean salinity from sensors can be difficult, lead to discrepancies
- End drops in salinity and pH could be burials
- Can examine brief window of environmental data one or two days before sampling
- Number of low tides could be interesting
- ex. Lots of drops in salinity at WB –> could number of low tides affect protein expression?
- Eelgrass extent as an explanatory variable
- Global eelgrass effect could override any bare sites?
- Biomarker data
- Ignore fatty acid data for now since there’s a low sample size
- Final height is a proxy for growth
- FB grew the most, CI grew the least
- Tissue mass highest in FB, then PG. WB, SK, and CI were similar
- Figure out biomarker comparison table code
- Scrub data
- Make environmental variable table
- Standard deviation/variance
- Number of observations above/below SD/2 SDs
- Number of exposures/low tides
- Days exposed
- Total time exposed
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Some more regressions
I tried to write a for loop in this R script to make a table with each peptide vs. biomarker comparison, R-squared value, and slope……but I’m hardcore struggling with it. I wrote a for loop within a for loop to create all of the plots, but now I can’t write another for loop to take all of the information I’m generating and put it in a new dataframe. I’m going to keep trying though!
After looking at my peptide vs. biomarker regressions, Steven suggested I make the same plots for each site. I used the R script linked above to do that. The plots can be found in these folders:
Port Gamble Bay
Skokomish River Delta
Once I figure out how to get my triple for loop to work, I’ll make a table for this information too. Now I guess I’ll wait for our meeting with Micah and Alex to see what to do next.
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