Information-Theoretic Mass Spectral Library Search

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Outline Introduction Related Work Method Results and Discussion. Information-Theoretic Mass Spectral Library Search. Arvind Visvanathan CSCE 990 Seminar in Multi-Dimensional Chromatography Systems, Informatics, and Applications. Information-Theoretic Mass Spectral Library Search. - PowerPoint PPT Presentation

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Information-Theoretic Mass Spectral Library Search

Arvind Visvanathan

CSCE 990Seminar in Multi-Dimensional Chromatography Systems, Informatics,

and Applications

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Outline

• Introduction– Mass spectrum search types

• Related Work– Other techniques

• NIST, PBM, DotMap

• Method– Probability and Information– Normalized distribution function

• Results• Conclusion

OutlineIntroduction

Related WorkMethod

Results and Discussion

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

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Introduction – Mass Spectrum

Mass SpectrumSearch AlgorithmSearch TypesApplications

OutlineIntroduction

Related WorkMethod

Results and Discussion

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

m/z

Inte

nsity

Decane

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Introduction – Mass Spectrum Search

OutlineIntroduction

Related WorkMethod

Results and Discussion

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MS Library

Unknown Spectrum Search

Algorithm

Pot

entia

l Mat

ches

Mass SpectrumSearch AlgorithmSearch TypesApplications

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Introduction – Search Types

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

• Identity search– Unknown mass spectrum present in library– Looking for exact spectrum

• Similarity search– Unknown mass spectrum not present in library– Looking for similar spectrum

Mass SpectrumSearch AlgorithmSearch TypesApplications

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Introduction – MS Search Applications

• Steroid detection in athletes• Monitor patient breath during surgery• Composition of molecular species found in

space• Honey adulterated with corn syrup• Locate oil deposits• Monitor fermentation process in the

biotechnology industry• Detect dioxins in contaminated fish

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

Mass SpectrumSearch AlgorithmSearch TypesApplications

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Related Work – NIST MS-Search [Stein ‘94]

• Pre-search the unknown spectra in library– Reduce search domain (160K 4K compounds)

• Compute match factor for each compound in the pre-search result

• Match Factor (MF)– Range 0-999– Higher the better

• Pre-search result sorted based on MF value• Pick the topmost compounds as possible matches

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MS SearchProbability Based MatchingDotMap

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Related Work – NIST MS-Search [Stein ‘94]

• Match Factor Computation [Stein ‘94]– Term 1 – Mass weighted normalized dot product

– Term 2 – Relative intensities of adjacent peaks in both spectra

– Combination of F1 & F2

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MS SearchProbability Based MatchingDotMap

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Related Work – NIST MS-Search [Stein ‘94]

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MS SearchProbability Based MatchingDotMap

OutlineIntroduction

Related WorkMethod

Results and Discussion

m/z Intensity

35 100

36 1

37 1

45 999

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m/z Intensity

35 100

36 1

37 2

45 999

55 200

C-1 C-2

Compare

C-1 & C-1

Compare

C-1 & C-2

F1 999 999

F2 999 824

MF 999 925

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Related Work – Probability Based Matching [McLafferty et. al. ‘75]

• Confidence Value (K) instead of MF• Four components for each m/z

– Term 1 : U : Based on the uniqueness of a m/z value– Term 2 : A : Intensity contribution to the confidence– Term 3 : W : Window factor (measure of agreement)– Term 4 : D : Dilution factor (measure of purity)– K ∑ (U + A + W – D) for each m/z

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

MS SearchProbability Based MatchingDotMap

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Related Work – DotMap [Sinovec et. al. ‘04]

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

MS SearchProbability Based MatchingDotMap

Fumaric acid

Adipic acid

Lactic acid

DotMap

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Related Work – DotMap [Sinovec et. al. ‘04]

• Inverse problem• DotMap computed across the image

• Higher valued areas indicate presence of compound of interest

• Multiple compounds of interest– Compute DotMap overlay

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

MS SearchProbability Based MatchingDotMap

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Related Work – DotMap [Sinovec et. al. ‘04]

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

MS SearchProbability Based MatchingDotMap

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Related Work – DotMap [Sinovec et. al. ‘04]

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OutlineIntroduction

Related WorkMethod

Results and Discussion

MS SearchProbability Based MatchingDotMap

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Method – Motivation

• NIST MS-Search [Stein ‘94]– No domain information utilized

• PBM Matching [McLafferty et. al. ‘75]– Old technique (‘75)– Ad hoc domain information utilization

• DotMap– No domain information utilized

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Method – Entropy

• Entropy based approach– Entropy measure of the amount of

uncertainty – Based on probabilities

• Include domain based knowledge (information) in computing the match factor

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Method – Distribution Function

• Library– NIST EPA Library– 163K compounds

• Compute distribution function (DF)– 2 dimensional array

• m/z vs intensity

– DF[i][j]• # compounds in library

– m/z = i– Intensity = j

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Method – Distribution Function

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

m/z

Inte

nsity

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Method – Normalized Distribution Function (NDF)

• Normalized Distribution Function

– NDF[mz][int] = DF[mz][int] / ∑ DF[mz][i]

– Where ∑ DF[mz][i] = 163K

– NDF Probabilities [0-1]

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

i

i

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Method – Assumptions

• AssumptionEach m/z is treated independently in the match

factor computation from normalized distribution function

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Method – Match Factor

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

MotivationProbability & EntropyDistribution FunctionMatch Factor

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Overview

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

• Technique– Compound in library + Noise – Search noisy compound in library

• Evaluation metric - Average Rank– Rank = Position of correct compound in hit list– Repeat above 3000 times and take average rank

• Compared with– NIST– NISTDOT (First term in NIST algorithm)

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Results – Noise models

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

• AdditiveAU = AL + G(0,σ)

• MultiplicativeAU = AL + AL* G(0,σ)

• Johnson ColoredAU = AL + G(0,σ*√m)

• Random spectrumAU = AL + x * AR

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Results – Additive Noise

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

• Compound = Compound + Additive noise• Additive Gaussian noise

– Zero mean– Variable standard deviation

• For each m/z in library spectrumAU = AL + G(0,σ)

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Additive Noise (Example)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Additive Noise (Performance)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Multiplicative Noise

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

• Compound = Compound + Multiplicative noise

• Multiplicative Gaussian noise – Zero mean– Variable standard deviation

• For each m/z in library spectrumAU = AL + AL* G(0,σ)

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Multiplicative Noise (Example)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Multiplicative Noise (Performance)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

30

Results – Johnson Colored Noise

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

• Compound = Compound + Colored Noise• Gaussian noise

– Zero mean– Variable standard deviation

• For each m/z in library spectrumAU = AL + G(0,σ*√m)

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Johnson Colored Noise (Example)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Johnson Colored Noise (Performance)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Random Spectrum Noise

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

• Compound = Compound + Random Spectrum

• Additive Spectrum– Add x% of another random spectrum

• For each m/z in library or random spectrum– AU = AL + x * AR

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Random Spectrum Noise (Example)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85

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Noisy

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Results – Random Spectrum Noise (Performance)

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

36

Results – Summary of Noise Models

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

• AdditiveAU = AL + G(0,σ)

• MultiplicativeAU = AL + AL* G(0,σ)

• Johnson ColoredAU = AL + G(0,σ*√m)

• Random SpectrumAU = AL + x * AR

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Results – Summary of Noise Models

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

-200

-150

-100

-50

0

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100

27 29 38 39 41 42 43 50 51 52 55 56 57 71 74 76 77 78 79 85

m/z

Inten

sity

Additive

Multiplicative

Johnson

Random

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Results – Summary of Noise Models

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

OverviewAdditive NoiseMultiplicative NoiseJohnson Colored NoiseRandom Spectrum Noise

OutlineIntroduction

Related WorkMethod

Results and Discussion

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m/z

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sity

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Multiplicative

Johnson

Random

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Conclusion

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

• MS library search algorithm• Information theoretic

– Domain knowledge incorporated

• Algorithm works well for various noise models

• Future work– Must improve performance for the random

spectrum noise case

OutlineIntroduction

Related WorkMethod

Results and Discussion

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Questions & Suggestions

Information-Theoretic Mass Spectral Library Search CSCE 990 – GCxGC Seminar

?

OutlineIntroduction

Related WorkMethod

Results and Discussion