Laura’s Notebook: Interesting Proteins, part II

To determine over/under-expressed proteins eelgrass vs. bare treatments I did the following:

1) Replaced all “N/A” values with blanks:

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2) Created a pivot table to sum the total peak area for each protein, broken down by sample #.

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3) I copied the pivot table results and pasted into a new tab; I did this b/c oddly referencing the pivot table in a subsequent formula was not working. Entered the Total Ion Current (TIC) values into

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4) On a new tab, I assigned each sample # its corresponding site & treatment, then normalized the protein sum peak area by the TIC: %TIC = [peak area / TIC]*100 I highlighted cells with %TIC between 20%-99% in green, and those >100% in red. I’ll need to investigate why I have some proteins with a peak area than the TIC.

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5) On a new tab, I averaged technical replicates’ %TIC (i.e. same sample was run twice on Lumos), then calculated the fold change in eelgrass beds compared to bare beds, by SITE. For example: %TIC @ Case Inlet = Average %TIC CI-Eelgrass / %TIC CI-Bare

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6) On a new tab, created another pivot table showing fold change for each protein in eelgrass beds compared to bare, organized by site. I highlighted proteins over-expressed by 5x in green, and under-expressed by 5x in red.

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7) Then, I did some more re-organization and extracted a list of proteins in each site that were over/under expressed 5x:

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8) I moved this list to a new (much smaller) file. Next task: identify which proteins are consistently expressed differently in the two treatments. I re-structured the list of proteins & associated fold change into 1 column, and assigned each data point a site. Then, I created yet another pivot table to organize these candidate “interesting” proteins by site; As shown in this screen shot, in total there are 2382 proteins that were over/under expressed by a factor of 5 in at least one site: 866 in Case Inlet, 1153 in Fidalgo Bay, 910 in Port Gamble, and 666 in Skokomish.

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TBD …

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