1. Novel Algorithms for the Quantification Confidence in Quantitative Proteomics with Stable Isotope Labeling* Novel Algorithms for the Quantification Confidence in Quantitative Proteomics with Stable Isotope Labeling* Chongle Pan 1,2 ; David L. Tabb 1 ; Dale Pelletier 1 ; W. Hayes McDonald 1 ; Greg Hurst 1 ; Nagiza F. Samatova 1 ; Robert L. Hettich 1 ; 1 Oak Ridge National Laboratory, Oak Ridge, TN 2 Genome Science and Technology, UT-ORNL * Research support provided by the U.S. Department of Energy, Office of Biological and Environmental Research.

2. Uncertainty in the Measurements Mass spectrometric measurement of a protein Mr = 23,564 Da ±10 Da 95% confidence Relative quantification of a protein in quantitative proteomics Abundance ratio = 1:1 95% confidence interval = [2:1, 1:2] The principal aim RelEx 1, ASAPratio 2, XPRESS 3, MSQuan 4 1 Anal Chem, 2003. 75: p. 6912-21 2 Anal Chem, 2003. 75: p. 6648-57 3 Nat Biotechnol, 2001. 19: p. 946-51 4 Nat Biotechnol, 2004. 22: p. 1139-45.

3. Experimental 1. Metabolic labeling of Rhodopseudomonas palustris with the stable isotope 15 N 2. Standard mixtures of natural and 15 N-labeled proteomes at the pre-determined mixing ratios 3. Shotgun proteomics analysis –MS instrument: linear ion trap (LTQ, Finnigan) –2D-LC method: 24-hour MudPIT technique 5 4. Protein identification –Database searching: DBDigger 6 –Identification filtering: DTASelect 7 5 Int. J. of Mass Spec. 2002. 219: p. 245-251. 6 Anal Chem, 2003. 75: p. 6912-21 7 J. Proteome Res. 2002 1: p. 21-26.

4. Benchmark Data Peptide I.D. filtering: 95% of true positive rate Protein I.D. filtering: minimum of 2 peptides Data quality Reproducibility

5. MS1 or mzXML format SIC reconstruction peak detection peptide quantification protein quantification maximum likelihood estimation principal component analysis parallel paired covariance Block Diagram selected ion chromatogram mass spectral data chromatographic peak peptide abundance ratio confidence score protein abundance ratio confidence interval

6. Peak Detection covariance scan number ion intensity Light isotopologue SIC; Heavy isotopologue SIC S/N=3; S/N=13 S/N=42 Parallel paired covariance chromatogram (PPC) Peak boundaries are defined as the local minima in the PPC, which include all MS/MS matching the peptide Peak boundaries

7. Peptide Quantification Peptide abundance ratios can be estimated by  Peak height ratio scan number ion intensity scan number  Peak area ratio ASAPratio, MSQuan, XPRESS

8. heavy isotopologue ion intensity light isotopologue ion intensity Peptide Quantification ion intensity scan number RelEx Linear regression RelEx ratio = tan( θ ) θ PC 1 PC 2 Principal component analysis (PCA) signal-to-noise ratio = PCA-SNR θ

9. Quantification Accuracy Peptide counts log 2 (ratio) Expected log 2 (ratio) Peak height ratio Peak area ratio PCA/linear regression 1:5

10. 5:1 10:1 1:10 1:1 log 2 (ratio) Peptide counts log 2 (ratio) Quantification Accuracy 1:15:1 1:51:10 10:1

11. Quantification Confidence log 2 (ratio) log 2 (PCA-SNR) peptide counts 5:1 2D histogram of peptide log 2 (ratio) & log 2 (PCA-SNR)

12. Quantification Confidence log 2 (ratio) log 2 (PCA-SNR) 5:1 Bin the peptides by their log 2 (PCA-SNR) value Bias: the deviation of the average estimated log 2 (ratio) from the expected log 2 (ratio) Bias increases as PCA-SNR decreases below a threshold

13. Quantification Confidence log 2 (ratio) log 2 (PCA-SNR) 5:1 Bin the peptides by their log 2 (PCA-SNR) value Variance: the variability of the estimated log 2 (ratio) Variance increases as PCA-SNR decreases

14. Quantification Confidence 1:5 log 2 (ratio) log 2 (S/N) Comet-like two-dimensional distribution As log 2 (SNR) decreases, the spread of log 2 (ratio) estimates increases the spread of log 2 (ratio) estimates increases the average of log 2 (ratio) estimates regresses to zero the average of log 2 (ratio) estimates regresses to zero log 2 (ratio) log 2 (PCA-SNR) log 2 (ratio) log 2 (PCA-SNR) 1:1 1:10 10:1 1:1 5:1

15. Quantification Confidence log 2 (PCA-SNR) | mean { log 2 (ratio) } | 5:1&1:510:1&1:10 1:1 log 2 (PCA-SNR) standard deviation { log 2 (ratio) } 1:1 5:1&1:5 10:1&1:10 The quantification bias and variance for peptides are linear functions of PCA-SNR

16. Protein Quantification log 2 (ratio) log 2 (PCA-SNR) mean Maximum likelihood point estimate of a protein’s abundance ratio is the ratio that best explains its measured peptides’ estimated log 2 (ratio) at the calculated log 2 (PCA-SNR) 2 sd measured peptides A series of theoretical probability distributions of peptide abundance ratio estimates at each PCA-SNR level

17. Quantification Accuracy RelEx filtering: > 0.7 correlation at 1 > 0.4 correlation at 10 > 3 signal-to-noise > 2 peptides log 2 (ratio) protein counts MSE: Mean Square Error PRATIO filtering: > 2 PCA-SNR > 2 peptides < 4 95% confidence interval width for log 2 (ratio)

18. 5:1 1:10 RelEx: red; PRATIO: blue Quantification Accuracy log 2 (ratio) 1:1 5:1 1:5 1:10 10:1 protein counts

19. Confidence Interval Estimation log 2 (ratio) 1:5 Display of the point estimates (+) and the 95% confidence interval estimates ( ----------- ) for protein abundance ratios Protein

20. Confidence Interval Estimation Point estimates and confidence interval estimates of protein abundance ratios log 2 (ratio) 1:11:5 5:110:1 1:1 1:10

21. Conclusions Three novel algorithms  Parallel paired covariance for peak detection  Principal component analysis for peptide quantification  Maximum likelihood estimation for protein quantification Improved Protein Quantification Accuracy Rigorous Confidence Interval Estimation Three novel algorithms  Parallel paired covariance for peak detection  Principal component analysis for peptide quantification  Maximum likelihood estimation for protein quantification Improved Protein Quantification Accuracy Rigorous Confidence Interval Estimation The fully automated program with graphic user interface is freely available for testing by contacting C. Pan (email: panc@ornl.gov)