7 Data Visualization for Mass Spectrometry

Effective visualization is crucial for understanding MS data, identifying patterns, and communicating results. This chapter covers comprehensive visualization techniques using the R for Mass Spectrometry infrastructure.

7.1 Setting Up Visualization Environment

Note: Using synthetic data due to mzR compatibility issues
Error details: BiocParallel errors
  1 remote errors, element index: 1
  0 unevaluated and other errors
  first remote error:
Error in DataFrame(..., check.names = FALSE): different row counts implied by arguments


Dataset summary:

Total spectra: 100

MS levels: 1, 2

RT range: 50 3500 seconds

7.2 Spectral Data Exploration

7.2.1 Core Spectral Variables

Understanding the key variables in Spectra objects is essential for effective visualization:

Core spectral variables:

 [1] "msLevel"                 "rtime"                  
 [3] "acquisitionNum"          "scanIndex"              
 [5] "dataStorage"             "dataOrigin"             
 [7] "centroided"              "smoothed"               
 [9] "polarity"                "precScanNum"            
[11] "precursorMz"             "precursorIntensity"     
[13] "precursorCharge"         "collisionEnergy"        
[15] "isolationWindowLowerMz"  "isolationWindowTargetMz"
[17] "isolationWindowUpperMz"

  msLevel  rtime precursorMz collisionEnergy polarity
1       1  50.00          NA              NA        1
2       1  84.85          NA              NA        1
3       1 119.70          NA              NA        1
4       2 154.55    1190.050        22.58851        1
5       2 189.39    1338.415        37.03558        1
6       1 224.24          NA              NA        1

7.2.2 TIC and BPC Visualization

7.3 Single Spectrum Visualization

7.3.1 Enhanced Spectrum Plots

7.3.2 Interactive Spectrum Visualization

7.4 Spectral Comparison and Mirror Plots

7.4.1 Mirror Plot Implementation

7.5 Chromatographic Visualizations

7.5.1 Total Ion Chromatogram (TIC)

7.5.2 Base Peak Chromatogram (BPC)

7.5.3 Extracted Ion Chromatogram (EIC)

7.6 Heat Maps and 2D Visualizations

7.6.1 m/z vs Retention Time Heat Map

7.7 Interactive Visualizations

7.7.1 Interactive Spectrum Plot

7.7.2 Interactive Chromatogram

7.8 Specialized MS Visualizations

7.8.1 Mass Defect Plot

7.8.2 3D Visualization

7.9 Quality Control Visualizations

7.9.1 Intensity Distribution

7.9.2 MS Level Distribution

7.10 Exercises

Create a function to generate spectral annotations with peak labels
Develop a multi-panel visualization showing TIC, BPC, and selected EICs
Implement a peak picking visualization with adjustable thresholds
Create an interactive dashboard for MS data exploration
Design visualization templates for different types of MS experiments

7.11 Summary

This chapter covered comprehensive visualization techniques for MS data, from basic spectral plots to advanced interactive visualizations. Effective visualization is essential for data exploration, quality assessment, and result communication in mass spectrometry analysis.