Generalized Protein Parsimony
and Spectral Counting
for Functional
Enrichment Analysis
Nathan Edwards
Department of Biochemistry and
Molecular & Cellular Biology
Georgetown University Medical Center
Systems Biology
Structured
High-Throughput
Experiments
2
Knowledge
Databases
Systems Biology
molecular biology
↕
phenotype
molecular biology
↕
biology
Structured
High-Throughput
Experiments
Knowledge
Databases
•
•
•
•
Proteomics
Sequencing
Microarrays
Metabolomics
3
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Localization
Function
Process
Interactions
Pathway
Mutation
Systems Biology
molecular biology
↕
phenotype
molecular biology
↕
biology
Structured
High-Throughput
Experiments
Knowledge
Databases
•
•
•
•
Proteomics
Sequencing
Microarrays
Metabolomics
4
Mathematical
Models
•
•
•
•
•
•
Localization
Function
Process
Interactions
Pathway
Mutation
Systems Biology
molecular biology
↕
phenotype
molecular biology
↕
biology
Structured
High-Throughput
Experiments
Knowledge
Databases
•
•
•
•
Proteomics
Sequencing
Microarrays
Metabolomics
5
Functional
Annotation
Enrichment
Mathematical
Models
•
•
•
•
•
•
Localization
Function
Process
Interactions
Pathway
Mutation
Systems Biology
molecular biology
↕
phenotype
molecular biology
↕
biology
Structured
High-Throughput
Experiments
Knowledge
Databases
•
•
•
•
Proteomics
Sequencing
Microarrays
Metabolomics
6
Functional
Annotation
Enrichment
Mathematical
Models
•
•
•
•
•
•
Localization
Function
Process
Interactions
Pathway
Mutation
Systems Biology
molecular biology
↕
phenotype
molecular biology
↕
biology
Structured
High-Throughput
Experiments
Knowledge
Databases
•
•
•
•
Proteomics
Sequencing
Microarrays
Metabolomics
7
Functional
Annotation
Enrichment
Mathematical
Models
•
•
•
•
•
•
Localization
Function
Process
Interactions
Pathway
Mutation
Functional Annotation
Enrichment
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In any draw, we expect:
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~ 5 "evens", ~ 2 "≤ 10", etc.
Each ball is equally likely
Balls are independent
p-value is surprise!
For transcriptomics:
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Genes
Genome
Diff. Expr.
Annotation
↔ Balls
↔ Tumbler
↔ Draw
↔ "evens",…
Draw 10 of 50!
8
Why not in proteomics?
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Double counting and false positives…
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Proteomics cannot see all proteins…
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…due to traditional protein inference
…proteins are not equally likely to be drawn
Good relative abundance is hard…
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…extra chemistries, workflows, and software
…missing values are particularly problematic
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In proteomics…
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Double counting and false positives…
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Proteomics cannot see all proteins…
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Use generalized protein parsimony
Use identified proteins as background
Good relative abundance is hard…
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Model differential spectral counts directly
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Ignore some PSMs
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FDR filtering leaves some false PSMs
Enforce strict protein inference criteria
Leave some PSMs uncovered
PSMs
Proteins
10%
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Ignore some PSMs



FDR filtering leaves some false PSMs
Enforce strict protein inference criteria
Leave some PSMs uncovered
PSMs
90%
Proteins
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Match uncovered PSMs to FDR
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Plasma membrane enrichment
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Pellicle enrichment of plasma membrane
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Six replicate LC-MS/MS analyses each
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Choksawangkarn et al. JPR 2013 (Fenselau Lab)
Cell-lysate (44,861 MS/MS)
Fe3O4-Al2O3 pellicle (21,871 MS/MS)
625 3-unique proteins to match 10% FDR:
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Lysate: 18,976 PSMs; Pellicle: 13,723 PSMs
89 proteins with significantly (< 10-5) increased counts
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Plasma membrane enrichment
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Na/K+ ATPase subunit alpha-1 (P05023):
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Transferrin receptor protein 1 (P02786):
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Lysate: 17; Pellicle: 63; p-value: 2.0 x 10-11
DAVID Bioinformatics analysis (89/625):
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Lysate: 1; Pellicle: 90; p-value: 5.2 x 10-33
Plasma membrane (GO:0005886) : 29 (5.2 x 10-5)
Transmembrane (SwissProtKW): 24 (1.3 x 10-6)
Transmembrane (SwissProtKW):
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Lysate: 524; Pellicle: 1335; p-value: 2.6 x 10-158
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A protein's PSMs rise and fall
together!
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A protein's PSMs rise and fall
together?
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Anomalies indicate
proteoforms
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Nascent polypeptide-associated
complex subunit alpha
7.3 x 10-8
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Pyruvate kinase isozymes M1/M2
2.5 x 10-5
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Summary
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Functional annotation enrichment for
proteomics too:
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Careful counting (generalized parsimony)
Differential abundance by spectral counts
Use (multivariate-)hypergeometric model for
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Differential abundance by spectral counts
Proteoform detection
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HER2/Neu Mouse Model of
Breast Cancer
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Paulovich, et al. JPR, 2007
Study of normal and tumor mammary tissue
by LC-MS/MS
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Peptide-spectrum assignments
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Normal samples (Nn): 161,286 (49.7%)
Tumor samples (Nt): 163,068 (50.3%)
4270 proteins identified in total
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1.4 million MS/MS spectra
2-unique generalized protein parsimony
Distribution of p-values (Yeast)
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