Plot MS Spectra

#Script to read in and plot MS Spectra from Shiadzu MALDI-TOF

#Step 1: Read files into dataframes

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

standardSpectra = read.table('A3_0001_A3_25-07-18_16-03_0001.txt', header = FALSE, skip = 8)
sampleSpectra = read.table('A4_0001_A4_25-07-18_16-04_0001.txt', header = FALSE, skip = 8)

#Step 2: Remove outlier in first row and add column names

standardSpectra = standardSpectra[-1,] #remove first row
standardSpectra = standardSpectra[standardSpectra$V1<900,] #set threshold
sampleSpectra = sampleSpectra[-1,]
sampleSpectra = sampleSpectra[sampleSpectra$V1<900,]
names(standardSpectra) = c('m_z', 'intensity') #name columns
names(sampleSpectra) = c('m_z', 'intensity')

#Step 3: Tidy data

sampleSpectra$type = 'Sample'
sampleSpectra$m_z = round(sampleSpectra$m_z) #round m/z values
standardSpectra$type = 'Standard'
standardSpectra$m_z = round(standardSpectra$m_z)
ms_data = rbind(sampleSpectra, standardSpectra)
names(ms_data) = c('m_z', 'intensity', 'type')

#Step 4: Visualize data using ggplot

ggplot(
  ms_data, aes(x = m_z, y = intensity))+
  geom_col(aes(colour = type , fill = type))+
  scale_color_manual(values = c("#ffc868", "#e5216b"))+
  scale_fill_manual(values = c("#ffc868", "#e5216b"))+
  xlab("m/z Ratio") + ylab("Intensity (mV)")+
  theme(legend.position="right")

#Step 5: Isolate sample type visuals using facet wrap

ggplot(
  ms_data, aes(x = m_z, y = intensity))+
  geom_col(aes(colour = type, fill = type))+
  scale_color_manual(values = c("#ffc868", "#e5216b"))+
  scale_fill_manual(values = c("#ffc868", "#e5216b"))+
  xlab("m/z Ratio")+
  ylab("Intensity (mV)")+
  facet_wrap(~type, nrow = 2)

#Find peaks with identical m/z ratios

matchSpectra = merge(sampleSpectra, standardSpectra, by = "m_z") #18 matches