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Reanalysis of Petricoin et al. Ovarian Cancer Data Set 3. Russ Wolfinger and Geoff Mann SAS Institute Inc. NISS Proteomics Workshop March 6, 2003. Ovarian Cancer Mass Spec Data from http://clinicalproteomics.steem.com. 91 Normals 162 Cancers. What We’d Love to See. What We Are Seeing. - PowerPoint PPT Presentation
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Reanalysis of Petricoin et al. Ovarian Cancer Data Set 3
Russ Wolfinger and Geoff Mann
SAS Institute Inc.
NISS Proteomics Workshop
March 6, 2003
Ovarian Cancer Mass Spec Data from http://clinicalproteomics.steem.com
91 Normals
162 Cancers
What We’d Love to See
What We Are Seeing
Green: Cancer, Red: Normal
Left: Green in Front, Right: Red in Front
New Paper from MD Anderson
Baggerly, K.A, Morris, J.S., and Coombes, K.R. (2003). Cautions about Reproducibility in Mass Spectrometry Patterns: Joint Analysis of Several Proteomic Data Sets
Email: [email protected]
• Reanalyses of all three ovarian cancer data sets
• For data set 3, they note that two pairs of m/z values provide perfect discrimination: 435.46 & 465.57, and 2.79 & 245.2. Easy to find with simple t-tests; genetic algorithm unnecessary.
First Pair: 435.46 and 465.57 Da
Green: Cancer, Red: NormalLeft: Green in Front, Right: Red in Front
Second Pair: 2.79 and 245.2 Da
Green: Cancer, Red: NormalLeft: Green in Front, Right: Red in Front
Questions
• What’s going on here?
• Are discriminators <500 Da generalizable?
• How about >500 Da?
Going Small: 435 Da
• At least 100 peptide fragments (including permutations) add up to 435, e.g. AFY, SMY, PPW, KNH, GGGAC, SSGGG
• 30 Hits from ChemFinder.com, including Sphingosyl-phosphocholine, a lipid molecule
• Similar kind of story for 465 Da
Going Large: Cross-Validated Stepwise Discriminant Analysis
1. Subtract baselines and determine 330 most prominent peak areas, all with m/z > 600.
2. Form 500 random partitions of the 253 spectra, with a 33% stratified holdout sample in each.
3. Perform stepwise discriminant analysis on each partition, using entry p = 0.05, exit p = 0.20, and max variables = 5.
4. Compute misclassification rate on each trial.
Results of Cross-Validated Stepwise Discriminant Analysis
1. Always picked 5 variables
2. Misclassification rate = 5%.
3. Most common discriminators:
• 681, appeared in 100% of selected quintuples
• 7379, in 63%
• 869, in 54%
• 4004, in 44%
Partial Least Squares on the Same 330 Peak Areas
Parting Shots
• Statistical discrimination is relatively easy for these data, but what are the real explanations for the clear differences in data set 3?
• Can statisticians overcome their biases and win the day?
• Is this kind of approach a red herring or a red snapper?
