Sam’s Notebook: DNA Isolation – C.gigas Ploidy Experiment Ctenidia

Isolated DNA from the remaining ctenidia tissue samples from Ronit’s experiment (Google Sheet).

Tissue was excised from frozen tissue block via razor blade (weight not recorded) and pulverized under liquid nitrogen. Samples were incubated O/N @ 37oC (heating block) in 350uL of MB1 Buffer + 25uL Proteinase K, per the E.Z.N.A. Mollusc DNA Kit (Omega) instructions.

After the O/N incubation, I processed the samples according to the E.Z.N.A. Mollusc DNA Kit (Omega) with the following notes:

Samples were eluted in 150uL of Elution Buffer.

Samples were stored in “Ronit’s gDNA Box #1 (positions A1 – B8 and C1 – E4)” in the FTR213 -20oC freezer.

See either of the spreadsheets for the full list of samples isolated today.

Kaitlyn’s notebook: clustering on real NSAF values

These plots were made using silo3and9_nozerovals_noincnstprot.csv.


Workflow for hierarchical clustering:

  1. Make dissimilarity matrix (used euclidean distance)
  2. hclust from cluster package to cluster
  3. Agglomeration coefficient (0.9991113) and cophentic correlation (0.9635755)
  4. Scree plot
  5. Dendrogram- find height to cut based on branching (50)
  6. 33 clustered with protein abundances
  7. Heatmaps- combined silos and indvidual silos.

scree-plot.jpeg4. Scree plot made from clustered data. The elbow is difficult to find therefore we make a dendrogram to find the best place to ‘cut’ the data.

dendrogram.jpeg5. Red line represents where the dendrogram was cut, and it was based on the area right before heavy branching occurred.

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  1. A total of 33 clusters were produced. The first image shows all proteins and the second is only the proteins that deferentially clustered. The proteins in the second image were used for the heatmaps.

50-unqprot-heatmap.jpeg

  1. Heatmap of deferentially clustering proteins.

These protein values are different between silos. It makes sense to plot them together on the heatmap to see this difference, but does clustering the proteins (that are different based on clustering) on the heatmap make sense? Because clustering is trying to group proteins in similar patterns together. I want to show similar patterns in Silo 3 and silo 9 separately, not together.

It looks like there are three main groups of abundance profiles found by clustering:

  1. The bottom group has fairly consistent abundance values between both silos but it looks like day 3 in silo 3 separated them. Abundance levels were very low for these proteins initially.
  2. The second group, in the middle, appears to have heavier expression at the end of the experiment in silo 9 compared to silo 3.
  3. The third group is the reverse of the second group where expression is greater in the beginning of the experiment in silo 3 compared to silo 9.

Additionally, would the abundance by cluster line plots complement this figure by showing the abundance based on group? Or is that redundant?


Silo 3

50-silo3-unqprot-heatmap

Silo 9

50-silo9-unqprot-heatmap

Does showing them individually, i.e., clustering the proteins individually, show the differences in abundance between silos better?

Shelly’s Notebook: Mon. Jan 16, 2019 ASCA on average NSAF values of filtered proteins

This entry refers to ASCA_avgNSAFvals_FilteredProteins.md; R markdown file here.

I redid ANOVA-simultaneous component analysis (ASCA) on average NSAF values of proteins that passed the filter for inconsistent detection in technical replicates (see yesterday’s post).

The PCAs generated by the ASCA look a little different than last time when I had used the ADJNUMSPEC vals, included day 15, and day 0 for both temperatures.

PCA for time (days)

new old
unnamed-chunk-7-1.png unnamed-chunk-6-2.png

PCA for temperature

new old
unnamed-chunk-8-1.png unnamed-chunk-6-1.png

I went ahead and did the analysis of proteins affected by temperature based on the PCA for temperature (‘PC1 vs PC2 for factor-combination 2’)(a), specifically looking at PC2 loadings values since this component shows the most separation between 23C and 29C. I made a heatmap plot (d) of proteins with PC2 loadings that fall within a cutoff I defined from loadings plots (b,c)

ManuscriptFigs_ASCA_temp_PC2.jpg

I added these methods and this figure into our draft manuscript.

It will be interesting to look deeper into what these proteins are that show higher abundance in 23C/ lower abundance in 29C; and vice-versa.

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