Kaitlyn’s notebook: MetboAnalyst “results” (not feasible with my data)

Univariate analyses

I’m going to go through the results I looked at based on the order you can choose them: 1)ANOVA, 2) Correlation Analysis, 3) Pattern Searching.

After you select ANOVA, this is the first plot Metbo shows you. I believe it is only useful for ms data. However, I chose Tukey’s test since it uses a pooled estimate for the variance and controls the family-wise error rate whereas Fisher’s LSD test is a series of pairwise t-tests and is not good for more than 3 groups.

The second plot gives me p-values for each protein. These p-values would tell me how different the proteins are from each other, but we have to remember that our silos are considered replicates here. So one protein is the pooled abundance from all three silos. This means that if two proteins were shown as different, they had significantly varied abundance when averaged across all silos.

This isn’t really answering my question. I would have to reorganize the data such that temperature the group however I do not have three replicates for each temperature and I could only include 1 silo since the proteins are duplicated for each silo.

After chatting with Roberto a little bit, I can see that none of these stats are going to be feasible with my data because of the lack of biological replicates. I’m going to post the results I got for future reference if I ever get the mass spec files or if someone else wants to know what Metbo can do (and because it took a lot of time to do this and interpret/understand the plots).

I’m going to move onto writing a script for ASCA.

ANOVA-1-TukeyANOVA-1-protein_by_proteinHere is the table this spits out.

  • This is the correlation heat map. It doesn’t make much sense to me or look very useful. correlation-1-nopatt
  • These are the plots for pattern analysis. This plot is supposed to measure the correlation between the temperature and proteins. The first plot has Pearson’s correlation coefficient on the X axis and each protein on the Y axis. Temperature is a perfect 1 since it is what we are correlating the proteins against. You can see the rst of the proteins listed correlate very well with temperature except for the last protein which has very little correlation with temperature.
  • The second plot is shows the “concentration” (or in this case abundance) of each protein and a table of the values for each protein which is here.pattern-1-optionspattern-1-table-protein

PCA analysis:

  • These are the PCA plots.




Cluster Analysis




Random Forest