Grace’s Notebook: Tuesday and Wednesday, December 19th and 20th, 2017

Today and yesterday I finished spinning down, and saving the supernatant and pellet of the final samples.

I added the labeled boxes to the -80 (Tray 14).

Pam picked up the 50µl samples this morning.

Yaamini’s Notebook: Remaining Analyses Part 15

The growth data wasn’t very interesting

Well, I guess it’s interesting. But just not for my proteomics story. In this R script, I looked at differences in percent growth by site.

growth-boxplot

Figure 1. Differences in percent growth by site.

Willapa Bay had the least variation in percent growth for just my samples. The ANOVA was significant, but a Tukey test revealed that the only significant pairwise differences were FB-CI (p = 0.0313908) and SK-FB (p = 0.0102010).

To see if the growth data could explain any differences in peptide abundance, I regressed peptide abundance against percent growth. I generated 37 scatterplots, which can be found in this folder. None of the R-squared values were any different from zero, so I didn’t put them into a new table.

My conclusion is that the growth data isn’t going to be a part of my paper’s story. One less thing to worry about!

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Yaamini’s Notebook: Remaining Analyses Part 14

I see the light

I think I’m almost done analyzing data! Which means I’m close to just sitting, entering my mental happy place, and writing away (yeah, I guess I’m one of those scientists that like to write) :relieved:

Here’s what took me 4.5 days to feel motivated and 1.5 days to do:

Downloaded tide data

I downloaded tide data from the links in this lab notebook entry. I used Union as my Skokomish River Delta Site, just as Micah suggested. The website allowed me to get tide data in 10 minute intervals, which perfectly aligns with the environmental data I have. I formatted and saved the tide data in this .csv file.

Removed exposure times from data

Based my meeting notes, we decided to use a one-foot clipping. This means that I could not use any pH, salinity, or dissolved oxygen data when the tide was less than one foot. Such a conservative measure would ensure that the probe readings we use will always come from one that is submerged in water. In this R Script, I replaced all probe readings from less than one-foot tides with “NA”. I then replaced any values greater than 1.5IQR + Third Quartile or less than First Quartile – 1.5IQR (i.e. outliers) with “NA”. Within R Studio, as.Date and the ability to find and replace within a highlighted selection were extremely useful.

I followed Steven’s suggestion to only use data from bare habitats, so I didn’t manipulate any data from eelgrass outplants. The only exception is that I needed to use data from the eelgrass habitat at Port Gamble Bay for salinity data. For some reason, there is no salinity data from the bare outplant at that site. Not sure how this will effect downstream analyses.

I saved the quality controlled pH, dissolved oxygen, and salinity in separate .csv files.

Remade boxplots and timeseries figures

New data means new figures, right? I used the quality controlled data for pH, dissolved oxygen and salinity. I considered removing outliers for the temperature data, but since the general consensus was to not mess with that data, I didn’t bother. If I need to, I have the code!

Temperature

R Script

temp-timeseries

temp-boxplot

Figures 1-2. Temperature time series and boxplot comparing temperature between sites.

Nothing different to see here! Temperature at Willapa Bay was higher on average when compared to Puget Sound sites.

pH

R Script

pH-timeseries

pH-boxplot

Figures 3-4. pH time series and boxplot comparing pH between sites.

Willapa Bay had the highest average pH, followed closely by Case Inlet. This is something I didn’t see before! The time series figure still shows wonky pH patterns towards the end of the outplant, possibly due to probe burial.

Dissolved Oyxgen

R Script

DO-timeseries

DO-boxplot

Figures 5-6. Dissolved oxygen time series and boxplot comparing dissolved oxygen between sites.

Willapa Bay had the lowest average dissolved oxygen content. Additionally, it had the least variability.

Salinity

R Script

salinity-timeseries

salinity-boxplot

Figures 7-8. Salinity time series and boxplot comparing salinity between sites.

Woah, Willapa Bay very clearly had the lowest salinity levels! There also seems to be some sort of geographical gradient with salinity that could be helpful for Laura’s work. The timeseries plot shows that we needed to remove a lot of the data from Fidalgo Bay due to the probe malfunctioning.

Variable of interest tables

I’ve learned that Steven likes tables, so I made some. I calculated at 12 different variables of interest at each site for each environmental variable in this R script. These are the variables I looked at:

  • Maximum
  • Minimum
  • Range
  • Mean
  • Variance
  • Standard Deviation
  • Percentage of data ± 2 SD
  • First Quartile
  • Median
  • Third Quartile
  • IQR
  • Percentage of data > 1.5IQR * Third Quartile and < 1.5IQR * First Quartile

I wrote out a different .csv file for each environmental variable:

Now I have something to share at tomorrow’s lab meeting! I’m also going to analyze the growth data from Micah with the hopes of sharing those results.

Back to R!

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Grace’s Notebook: Monday, December 18, 2017

Today I was in for a little over an hour labeling tubes in preparation for tomorrow and Wednesday. I will be spinning down the final batch of Tanner Crab hemolymph and RNAlater mixtures, and saving the supernatant and pellets in separate tubes in the -80.

IMG_0396 2

Grace’s Notebook: Friday, December 15, 2017

Today I set aside 50µl of crab hemolymph and RNAlater mixture for Pam. 117 tubes for the 117 crabs that remained at the end of the project. Storing them in labeled boxes in the refrigerator in Rm 213.

Next week I will spin down all the tubes, keep the supernatant and pellets in separate tubes and store in -80. Hoping that more P1000 pipet tips will arrive by then to help speed things up.

Grace’s Notebook: Thursday, December 14, 2017

(1) 2015 oyster seed data

I went through some more peptides on SkylineDaily with the 2015 oyster seed data. Protocol calls for checking ~100 peptides to ensure correct peak was selected (Step 5).

(2) Crab project

I met with Steven and Pam today to discuss the overall project and next steps.

Pam brought the most recent samples. There are three samples per crab, and 6 per warm-water treatment crabs (because only 3 survived). Total of 117 crabs.

I will save 50µl of hemolymph/RNAlater mixture for Pam from each crab (117 tubes total). Then I will spin down all the samples (114×3 + 3×6 = 360 samples total), remove and save the supernatant, and place supernatant and pellet tubes in -80.

In January, we will start processing the RNA and pooling the samples to be sent off for sequencing.

Yaamini’s Notebook: Remaining Analyses Part 13

Triple-nested for loops are not fun

As I mentioned in a previous post, Steven suggested I create a table with information about the peptide vs. biomarker regressions I did. I was trying to do that using a three for loops nested within eachother.

screen shot 2017-12-13 at 2 16 29 pm

It did not go well.

When I was talking with Sam today, he suggested I break the nested for loops. Instead, he thought I should save all of the regression information into a new table, and then reformat that information into the table I wanted to save. That was the right idea! The code can be found in this R Script, or below.

screen shot 2017-12-13 at 2 46 36 pm

screen shot 2017-12-13 at 2 46 51 pm

screen shot 2017-12-13 at 2 46 59 pm

The table I produced can be found here.

My next steps are to (finally) quality control my data, remake pH, DO and salinity plots, make a table of important environmental variables, and do something with the growth data. Oh, and write I guess.

…can you tell I’m a bit tired of this yet?

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Grace’s Notebook: December 12, 2017

Crab hemolymph samples:

Pink-capped were sampled 11/10/2017

I spun down the hemolymph samples (5000g for 10min) and saved the supernatant and pellets in separate tubes. The boxes were placed in the -80 (tray 14).

Laura’s Notebook: December Goals (belated)

Time flies, especially when I get various waves of data that needs analyzing, and also Thanksgiving, tests/papers from class, etc… Lots of tasks will carry over from November, BUT nevertheless, headway has been made.

Olys at dock

  • Clean, measure broodstock (after hatchery meeting)

Geoduck proteomics

  • Finish Results section
    • Correlation btwn various parameters, protein abundance
    • Try to use Structural Equation Model instead of linear regression model
    • QA %>1SD & %>2SD calculations
  • Get more methods info from Micah, Emma
  • Focus on DISCUSSION
  • Identify target journals
  • Determine figures for paper. Candidates:
    • Box/violin plots of protein abundances by subbasin, including 3 peptides separately. Like this, but with 4 panes for each protein: img_3305
    • NMDS plot (like what I already have) with overlaying circles by subbasin
    • 3-pane plot of pH, T, and DO (continuous)? Would be messy with all 8 traces from each site.
    • Correlation matrices for: variables identified in model (DO var, pH >1sd %, and growth)
  • Remaining things that Emma said I should do:
    • Linear Response Plot, as per Emma: Peak area on the y, amount of peptide (moles) on the x. Don’t know absolute quantity of experimental peptides, could make the x-axis relative quantity or something. Can generate plots like these in MSstats.
    • DIA error rate
    • SRM dilution curve
  • Idea for paper, but would take a while (probably a few days): screen all proteins in DIA Skyline data for a “menu” of targetable, detectable proteins (based on visual check of chromatograms). This could be published alongside the paper as a very usable resource.
  • More lit Research
    • Growth related to protein expression – but why only some proteins were diff. expressed?
    • Bioassays
    • Determine if abundance difference is “biologically relevant.” If I can find SRM abundance good (haven’t found good references yet), OR find citable % diff.

Prep for Oly genetics hatchery vs. wild analysis

  • re-Read Fischer et al. 2012
  • Get most current data set from Crystal
  • ID best program to use

Application things

  • Finish NDSEG, get letters
  • AA travel awards (?)

Misc.

  • Submit MS paperwork to office & proposal online (pending approval from Rick, Jackie)
  • Learn how to tag notebook posts
  • Create README.md for proteomics data on Owl

In January:

  • Determine which Oly samples to sequence from last spring/winter
  • Meet with STATS resource to figure out how to analyze Oly larval survival – parse out survival data to 224um, to juvenile, by time for reps

On hold:

  • Record podcast pilot with Megan
  • Find local vendor that sells dry suits, try on to determine size, then purchase gear

Accomplishments from last month

  • Checked out Oly histology data from last year’s experiment
  • Cireculated MS proposal for approval from committee
  • Submitted NSA abstracts, travel grant app
  • Sampled experimental Olys for histology (time=0), got animals into treatments
  • Received collection permit @ Mud Bay, made contact with homeowners for access, collected first batch
  • Gave Oly shells to Heater @ Wood’s lab for Polydora inspection
  • Got the CoEnv travel grant for AA
  • Proteomics stuff:
    • Finalized SRM tech. rep filtering process
    • Tracked down tidal charts from each Geoduck Proteomics ouplant location; used to process/filter environmental data from Micah
    • Generated summary statstics on environmental data
    • Re-ran proteomics analyses on the peptide-level, where transitions within each peptide were summed. Lambda-transformed peptide abundance for normal distribution, then ran 2-way ANOVAs.
    • Learned how to run stepwise linear resgression models, ran on diff. expressed proteins with environmental summary stats.
    • Refined methods section of paper
    • Made headway on results section of paper
    • Drafted intro; will likely need to revise based on results
    • Finished parasite class things
    • Generated notebook for DIA data in Skyline
    • R-script for merging DIA results with annotations, extracting SRM targets from this dataframe
    • Generated peptide variability stats for paper
    • Discarded unecessary vials (the “just in case” vials, and autosampler vials prepped for mass spec). If I need to re-run samples, I’d prepare new autosamper vials from my digested peptides.

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Grace’s Notebook: December 11, 2017

Crab Hemolymph Samples:

Green-capped were sampled 11/08/2017

I spun down the crab hemolymph samples and saved the supernatant and pellets in separate boxes. I spun them down at 5000g for 10 minutes. Then, I placed the boxes in the trays in the left -80. (Tray 8 and Tray 10)

I only got through the green-capped samples today.