Laura’s Notebook: SRM data – quantfying tech. rep. quality

Today I figured out how to calculate distances between tech reps on the NMDS plot to numerically validate my removal of poor-quality reps. I ended up removing a few more reps (as compared to visually inspecting reps), but as a whole not much has changed. I also generated a couple plots using Plotly, which is fantastic. Plotly creates interactive plots so you can hover over points, zoom into a plot, etc.

Here is a plot of my technical replicate NMDS:

tech rep

If you download this file and drag into your browser you can view it in Plotly: tech rep plotly (couldn’t quickly figure out how to render Plotly in my notebook; if you know how please let me know in comments!)

R script written to calculate euclidian distances between technical replicates on NMDS & plot via Plotly:

 #### Calculate distances between tech rep points on NMDS plot and plot to ID technical rep outliers library(reshape2) srm.nmds.tech.distances <- NULL for(i in 1:length(SRMsamples)) { G <- SRMsamples[i] D <- dist(SRM.nmds.samples.sorted[grepl(G, rownames(SRM.nmds.samples.sorted)),], method="euclidian") M <- melt(as.matrix(D), varnames = c("row", "col")) srm.nmds.tech.distances <- rbind(srm.nmds.tech.distances, M) } srm.nmds.tech.distances <- srm.nmds.tech.distances[!srm.nmds.tech.distances$value == 0,] #remove rows with value=0 (distance between same points) srm.nmds.tech.distances[,1:2] <- apply(srm.nmds.tech.distances[,1:2], 2, function(y) gsub('G|G0|G00', '', y)) #remove extraneous "G00" from point names library(ggplot2) library(plotly) p1 <- plot_ly(data=srm.nmds.tech.distances, y=~value, type="scatter", mode="text", text=~row) htmlwidgets::saveWidget(as_widget(p1), "NMDS-technical-replicate-distances.html") summary(srm.nmds.tech.distances$value) bad.tech.reps <- srm.nmds.tech.distances[srm.nmds.tech.distances$value>.2,] #which tech rep distances are >0.2 View(bad.tech.reps)  

Resulting “bad.tech.reps” were determined as distances >0.2 on NMDS plot/scale. This standard is still not good enough for publication; need to investigate NMDS stats to see if there is a sd, variance, or something that I can use to validate my 0.2 selection.
bad tech reps combinations >0.2

Plot of technical rep distances using Plotly: tech rep distances plotly

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Yaamini’s Notebook: Correlating Technical Replicates Part 2

Step 1: R-squared Cutoffs

In this R script, I used three different R-squared cutoffs to weed out transitions and reexamine my technical replication. I tried the combinations of normalized and nonnormalized data with different cutoffs. I made NMDS plots for each option and calculated the distances between my technical replicates.

Cutoff = 0.6, nonnormalized data:

0.6-nonnormalized-NMDS

0.6-nonnormalized-distances

Cutoff = 0.6, normalized data:

0.6-normalized-NMDS

0.6-normalized-distances

Cutoff = 0.7, nonnormalized data:

0.7-nonnormalized-NMDS

0.7-nonnormalized-distances

Cutoff = 0.7, normalized data:

0.7-normalized-NMDS

0.7-normalized-distances

Cutoff = 0.8, normalized data:

0.8-normalized-NMDS

0.8-normalized-distances

I found that normalizing my data made my NMDS plots look better, so I didn’t use an 0.8 cutoff with nonnormalized data. Overall, my NMDS plots looked better, but they’re still not fantastic. With a 0.6 cutoff, I had 88 transitions, 45 with a 0.7 cuttoff, and 17 with a 0.8 cutoff. I personally think the 0.7 cutoff is a happy medium between losing too much data on proteins and a better NMDS plot. I still need to try the x = y slope method outlined in this issue. For now I’ll update Emma and Steven and see what they think.

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Laura’s Notebook: Cleaning day – rinsing Olys and cleaning OA system

It’s been 1 week since I moved my Oly seed to the dock; today I checked on them to ensure the screen envelopes are still secured and to rinse them with fresh water. Everything was still in place and the screen wasn’t too dirty, so I’ll wait ~10 days to 2 weeks to return. I also tagged the Oly cages 92. NOTE: there are three cages hanging together on 92; my Oly broodstock and some of Yaamini’s gigas are in 2 cages, and my Oly seed are in the other.

OlyTag92OlyCages92Seed PacketsSeed Packets

I also cleaned up the OA space in the hatchery and distributed the various materials that were sitting back there. Next time I go out I need to clean various tubes that are soaking in bleach, but after that I won’t need to tend that space again until we use it again.

Manchesterclean

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Yaamini’s Notebook: Correlating Technical Replicates

Are some transitions worse than others?

Short answer: Yes.

Long answer: Emma suggested that I regress my second batch of technical replicates against my first batch to see if there were certain transitions that are messier than others. I used this R script to plot the regressions. The final plots can be found in this folder.

*Random gold star moment 1: My for loop for making all my plots worked the first time I wrote it #WIN * *Random gold star moment 2: I used a relative path to set my working directory #LEARNING *

I added the adjusted R-squared value to the top left corner of each plot. There are definitely potential outliers and leverage points in each plot, and some transitions have higher R-squared than others. I also see that some samples are continually those potential outliers and leverage points. Generally, the three transitions associated with each peptide have the same R-squared values.

bad-R

Figure 1. Transition with the lowest adjusted R-squared value.

good-R

Figure 2. Transition with one of the higher adjusted R-squared values.

The next step is to consult Emma and Steven to create a selection criteria. Here are two ideas:

  • Establish an R-squared cutoff. Any transitions with adjusted R-squared values lower than the cutoff should be eliminated.
  • Identify outliers and leverage points in each plot. Remove these points and re-plot to see if the R-squared value increases.

My guess is that we’ll use some combination of methods to determine which transitions to keep. While I work on this, I’m also reviewing my SRM protocol for reproducibility and making an NMDS plot with just the PRTC peptides to see if that provides us with any additional information.

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Grace’s Notebook: Tuesday, October 10, 2017

Today I added some hyperlinks to Capstone papers from previous students to the SAFS Capstone page. 

Then, I worked on some Skyline Daily things – working on going through Laura’s and Yaamini’s SRM protocols.

Titrator:

The new power connecter cord came and it’s amazing! I don’t understand why the other design exists.

Things are pretty much set-up now, but I’m unsure how it all works together. So I created some requests to seek out chemicals and supplies to run a test-run of the titrator.

Yaamini’s Notebook: Reproducible SRM Analysis

R&R: Review and Reproduce

Last week, I created a workflow for reproducing my SRM data and analyses. Steven went through the pipeline as much as he could and posted the following issues:

  1. Opening new Skyline file

I forgot to use the specifif verbage Skyline does! I asked the user to open a new Skyline document, which is the same as opening a blank document.

  1. Source of .blib?

Emma and her team made the .blib I used for my DIA and SRM analyses, but I did not explain how I got the file. I added the explanation Emma gave me, as well as a link to my original lab notebook entry with this information.

  1. Provide brief explanation of major steps

Because I guess it makes sense to let the user know what they’re doing and why they’re doing it…

  1. Fail at step 2d

Whoops. Skyline requires a FASTA file, but I linked a .txt file with the same information. On the Windows machine I found the corresponding FASTA file and uploaded it to owl. I also fixed the link in the protocol. When I tried following my instructions to populate the analyte tree (Step 2d), copying and pasting the sequence information did not work! Skyline daily has been kind of annoying with this in the past. To make things easier, I updated the instructions so the user would only have to import the file, not open and copy and paste sequences with varying levels of success.

Clearly no matter how explicit I think my instructions are, it’s always different when other people look at it! Laura and Grace are going to continue going through my protocol. Hopefully they catch something I didn’t and we figure out why my technical replication is funky.

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Grace’s Notebook: Monday, October 9, 2017

Titrator

Worked on the Titrator again… It was very frustrating because everything seemed right, except the Rondolino sample changer would not turn on!

Sam came over and checked it out. He moved the Rondolino to look at the back of it, and when he did that, it worked! The reason is because the power connection got pushed slightly by another cable, which put it in an angle that allowed for it to work! Kind of ridiculous, so Sam contacted Mettler Toledo and a new power connection cable will arrive tomorrow!

SRM Protocols

I continued testing Laura and Yaamini’s SRM protocols. Yaamini hasn’t had a chance to look at it since my last attempt, so I still had an issue with the first step of pasting the FASTA file into the lefthand window of Skyline Daily.

Laura’s is working great! I had a suggestion of perhaps adding some clarification at Step 5, Notebook 01 because I was a little confused when a window didn’t pop up in the order her protocol suggested it would. All the .raw files just downloaded, and I continued, but my final rearranged Skyline window doesn’t look like hers.