• Proteomics Data Analysis in R/Bioconductor
  • Welcome!
    • Miscellaneous Resources
  • 1 Heatmaps
    • 1.1 Expression Heatmaps
    • 1.2 Correlation Heatmaps
      • 1.2.1 Sample Correlation
      • 1.2.2 Feature Correlation
    • 1.3 Heatmap Annotation
      • 1.3.1 Modifying Default Colors
    • 1.4 Additional Modifications
      • 1.4.1 Change row or column labels
      • 1.4.2 Change label colors
      • 1.4.3 Label specific features
      • 1.4.4 Change heatmap body color
  • 2 Differential Analysis
    • 2.1 Linear Regression
    • 2.2 One-Way ANOVA
    • 2.3 t-tests
      • 2.3.1 One Comparison
      • 2.3.2 Multiple Comparisons
    • 2.4 p-value Histograms
    • 2.5 Volcano Plots
  • References

Proteomics Data Analysis in R/Bioconductor

Miscellaneous Resources

It is highly recommended to review the resources presented in this section before continuing with the rest of the tutorial.

  • Proteomics Overview
    • Protein Analysis by Shotgun/Bottom-up Proteomics
    • Modern Proteomics – Sample Preparation, Analysis and Practical Applications
    • Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

  • Mass Spectrometry

    • Warwick School of Life Sciences Teaching Animations
    • Tandem Mass Spectrometry for Peptide and Protein Sequence Analysis
    • Maestro: Comprehensive, Multi-Stage Spectrum Identification in Protein Mass Spectrometry
    • Searching databases for protein identification - part 1 (YouTube video)
    • Mass spectrometry for proteomics - part one (YouTube video)
    • Electrospray Ionisation Mass Spectrometry: Principles and Clinical Applications

  • PNNL’s Data Management System (DMS)

  • Integrative Omics PRISMWiki

  • Universal Protein Resource (UniProt): protein sequence and annotation data

  • False Discovery Rate (FDR)

    • How to talk about protein‐level false discovery rates in shotgun proteomics
    • Posterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin
    • False Discovery Rate: PEAKS FDR Estimation
    • False discovery rates in spectral identification

  • RStudio Cheatsheets

  • Pattern matching with regular expressions

    • R for Data Science: Strings
    • RegexOne: Learn Regular Expressions with simple, interactive exercises.