Shelly’s Notebook:

FFAR meeting

• Can’t untangle effects from high/low pH or static/fluctuating, so include additional fluctuating low pH treatment with new geoduck
  • Hollie has a code for this
• Why is the oxygen low on Nov 10? What is the saturation of water at 9-10C and salinity @ 29 PSU ?
• continuous feeding:
  • bringing peristatic pump
  • there are also programmable pumps

To-do on Friday Nov 16

1. water chemistry
   • discrete measurements
     • pH tris curve
   • titrator
     • pH cal
     • CRM
     • water samples
2. hemolymph samples from all animals
   • supplies needed:
     • liquid nitrogen carrier
     • at least 150 microfuge colored LABELLED tubes replace (need to order more)
     • needles
     • syringes
     • centrifuge (can we set the freezer to 4C and put it in there?)
     • styrofoam containers for carrying samples on ice to centrifuge (ice packs in freezer in garage)
     • p1000 and tips for transferring lymph
3. labels for animals (can do this while taking hemolymph)
   • maybe just mark with paint pen for now?
4. automate data download
   • enable remote access to router
   • test data download from dry lab building
5. calibrate temp probes
6. Set up tanks 5 and 6 with gas
   • need to order 4 more pumps
   • do we have probes for these?
7. Think about respirometry
   • Presens is expensive, and preferred to stay in the dry lab
   • carting water (for containers and water bath) is lots of labor
   • need a solution

Need to order:

• 4 submersible pumps (Drs. Foster & Smith)
• 1.7 mL microfuge tubes 500ct Rainbow Pack (VWR cat no. 87003-296) $24.14 (https://ift.tt/2DGCncd)
• calcium assay 96-well $215 (cayman chemical) https://ift.tt/2RSfmpC

Yaamini’s Notebook: MEPS Revisions Part 1

Revisiting NMDS for protein abundance data

According to Github it’s been exactly a year since I last touched my SRM data :0 But now I’m back and remaking NMDS plots! For the MEPS revision, reviewers have asked me to complete an analysis of the environmental data, and include the environmental data when looking at differential protein abundance. To do this, I’m going to conduct a constrained ordination analysis, in which I examine my protein abundance data, while constraining it for environmental variables in each site and habitat. Julian suggested I revisit my protein abundance data before I proceed with the constrained analysis, so here I am.

In this R Script (side note: can you believe I ever used plain R and not R Markdown?! I cannot wait to convert this script into an R Markdown file) I took my protein abundance data and first transformed it using a Hellinger’s transformation. The idea here is to control for any rare peptides or high instances of 0s by downweighting them in the analysis. Then, I used a euclidean distance matrix on the transformed data. Finally, I ordinated the data with an NMDS. I performed either a one- or two-way ANOSIM to assess the significance of groupings

Site and Habitat

For this ANOSIM, the R statistic was 0.088, meaning that within group and between group similarities were equal (p = 0.073). There is no evidence to refute the null hypothesis that site and habitat groupings exist.

Habitat only

Figure 1. Ordination of protein abundance data looking solely at habitat type. Confidence ellipses are used to demonstrate overlaps or segregation of data.

Once again, within- and between-group similarities between bare and eelgrass habitats were similar (R = 0.044, p = 0.122).

Site only

Figures 2-3. Ordination of protein abundance data looking solely at site designation. Oyster sample IDs for each site are either bounded by a polygon (top), or confidence ellipses are used to demonstrate overlapping nature of data (bottom). Both the polygon and confidence ellipse for Willapa Bay are slightly segregated from the other four sites.

Within- and between-group similarities between all five sites were similar (R = 0.064, p = 0.065). However, when I repeated the ANOSIM with region designation (Puget Sound vs. Willapa Bay), there was mild evidence for regional differences (R = 0.226, p = 0.053).

Loadings

I correlated the NMDS scores with the original data matrix to obtain loadings, then plotted only those that were significant at the 0.001 level. Turns out that’s a lot of peptides.

Figure 4. Peptide loadings significant at the 0.001 level.

I’ll need to revise the significance criteria to see if I can plot less loadings up there.

Going forward

1. Complete ordination analysis of environmental data
2. Meet with Julian to discuss unconstrained ordination results
3. Plan for constrained ordination approach

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Grace’s Notebook: Submitted GSS poster, and my goals for Th and F

Today at 4:25pm, I submitted my poster to be printed for GSS, which is tomorrow… Steven helped a lot with picking out what information would be interesting to include. My goals for Thursday and Friday include both the Crab Project and the 2015 Oysterseed Project.

GSS Poster

Google slides link to poster: here

I made the pie chart in excel really quick with this file: Blastquery-GOslim-sep.csv, which is the output file with columns tab delimited using R from this python notebook: 11052018-C_bairdi-blastn.ipynb.

To make a poster, you can use google slides and set the dimensions to 48in w x 36 in h (File > Page Setup > Custom > adjust dimensions.

Then, you export the slide as a PDF, and send it to UW Creative Commons.

Goals for the rest of the week:

2015 Oysterseed:

• mprophet model in Skyline (notes from Emma)

Crab Project:

• Make extraction plan for other libraries (get input from Sam and Steven)
• Extractions
• R script for adding new Qubit data

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