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:

`ASCA.calculate()`

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