Kaitlyn’s notebook: ASCA results

I used the new silo3_9 table I made for kmeans clustering and modified Shelly’s code to do the ASCA. Factor 1 is temperature and factor 2 is time.

Helpful tips:


A combination of variables can explain the total variation based on temperature (PC1 = 100% for temperature) while 52.15% of the variance is explained by a single principal component for time. The interaction of time and temp produce the least amount of explained variance for PC1 (42.20%).

The sum of squares model describes how well the data fits a linear regression. Centering the data is important because PCA is a regression model without intercept. Non-centered data can be misleading since the eigen vector may point in the appropriate direction. Time contributes to the variance the most at 63.84% if the data is centered.

Temperature loadings plot of PC1 and PC2:

Time loadings plot of PC1 and PC2:

Interaction loadings plot of PC1 and PC2:

Single Score plot for Temp:

Single score plot for Time:

Single score plot for interaction:

Score plot with projected data for Temp:

Score plot with projected data for Time:

Score plot with projected data for Interaction:

ASCA.DoPermutationTest() shows that no factors have a significant effect on protein abundance, but time has the greatest effect.
1(Temp): 0.119
2(Time): 0.015
12(Interaction): 0.191

Sam’s Notebook: SRA Submission – Olymia oyster Whole Genome BS-seq Data


Submitted our whole genome bisulfite sequencing data to NCBI Sequence Read Archive (SRA).

Relevant SRA info is below.

Have updated nightingales Google Sheet with SRA info.

SAMPLE SRA (Study) BioProject BioSample
1NF11 SRP163248 PRJNA494552 SAMN10172233
1NF15 SRP163248 PRJNA494552 SAMN10172234
1NF16 SRP163248 PRJNA494552 SAMN10172235
1NF17 SRP163248 PRJNA494552 SAMN10172236
2NF5 SRP163248 PRJNA494552 SAMN10172237
2NF6 SRP163248 PRJNA494552 SAMN10172238
2NF7 SRP163248 PRJNA494552 SAMN10172239
2NF8 SRP163248 PRJNA494552 SAMN10172240

from Sam’s Notebook https://ift.tt/2DWXc3V