#Compare number of proteins identified across technical replicates.  Tech reps are sorted in spreadsheet from proteins that are identified across all reps to those identified across the fewest reps.

setwd('/Users/emmatimminsschiffman/Documents/Dissertation/proteomics/DB post-genome')

techreps<-read.csv('tech reps for plotting.csv', header=T)

plot(techreps$Matches, techreps$Order, type='n', xlim=c(0,12), las=2, ylab='C. gigas Protein', xlab='Technical Replicates', yaxt='n', xaxt='n')
points(techreps$X221_1, techreps$Order, col='mediumblue')
points(techreps$X221_2, techreps$Order,col='mediumblue')
points(techreps$X222_3, techreps$Order,col='mediumblue')
points(techreps$X224_1, techreps$Order, col='goldenrod3')
points(techreps$X224_2, techreps$Order, col='goldenrod3')
points(techreps$X224_3, techreps$Order, col='goldenrod3')
points(techreps$X227_1, techreps$Order, col='forestgreen')
points(techreps$X227_2, techreps$Order, col='forestgreen')
points(techreps$X227_3, techreps$Order, col='forestgreen')
points(techreps$X230_1, techreps$Order, col='lightcoral')
points(techreps$X230_2, techreps$Order, col='lightcoral')
points(techreps$X230_3, techreps$Order, col='lightcoral')

#plot expression level (total spectral counts) versus number of matches (times protein was identified across technical reps)

spectral.dat<-read.csv('spec counts tech reps.csv', header=T)
#for total counts
plot(spectral.dat$matches, spectral.dat$total.spectra, ylab='Total Spectral Counts', xlab='Number of Times Protein was Identified', xlim=c(0,12))

#for average spectral counts
plot(spectral.dat$matches, spectral.dat$average.spectra, ylab='Average Spectral Counts', xlab='Number of Times Protein was Identified', xlim=c(0,12))