analysis of proteomics data using maldiquant of proteomics data using maldiquant sebastian gibb...

Analysis of Proteomics Data using MALDIquant

Sebastian GibbInstitute for Medical Informatics, Statistics and Epidemiology (IMISE)

University of Leipzig

17. August 2011

Sebastian Gibb, MALDIquant, 2011-08-17 1

Table of Contents

1 IntroductionProteomicsMass Spectrometry

2 Analysis of Proteomics Data using MALDIquantDesignSingle Spectrum WorkflowPreview

Sebastian Gibb, MALDIquant, 2011-08-17 2

Predicting disease

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Proteomics

Proteomics

• Study of the entirety of proteins produced by an organism.

• Foci: identification, structure determination, biomarker,pathways, expression.

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Mass Spectrometry

Ion Source: MALDI

Matrix-Assisted LaserDesorption/Ionization

Mass Analyzer: TOF

Time Of Flight (t ∝√

mq )

Detector

QuantityMeasurement

Abb. 3.14.; S. 67; “Biochemie & Pathobiochemie”, Loffler G., 8. Auflage (2007), Springer Medizin Verlag

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MALDI-TOF Example Spectrum

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MALDIquantMotivation

• Only relatively few open source software solutions availableand very few for the R platform.

• No MALDI-TOF package fitting our needs for clinicaldiagnostics.

• Necessity of handling both technical and biologicalreplicates.

• Unsatisfying quantification of relative intensities(total-ion-current, 0/1)

• Investigation of impact of calibration of spectra on clinicalprognosis.

• Modular and easy to customize analysis routines.

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MALDIquantThe UNIX philosophy: do one thing and do it well McIlroy (1978)

MALDIquant

single spectrum multiple spectra

peak alignmentcalibration/

normalization

smoothing

baseline correction

peak detection

raw data

readBrukerFlexData

...

readMzXmlData

classification

sda

randomForest

...

pamr

smoothing

baseline correction

peak detection

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MALDIquantThe UNIX philosophy: do one thing and do it well McIlroy (1978)

⇒

f1,1 f1,2 f1,3 . . . f1,n

f2,1 f2,2 f2,3 . . . f2,n

. . . . . . . . . . . . . . .fm,1 fm,2 fm,3 . . . fm,n

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MALDIquantStructure

object-oriented, S4

AbstractMassObject+mass: vector+intensity: vector+metaData: list

+as.matrix()+intensity()+intensityMatrix()+isEmpty()+length()+lines()+mass()+metaData()+plot()+points()+transformIntensity()

MassSpectrum

+detectPeaks()+estimateBaseline()+estimateNoise()+findLocalMaxima()+removeBaseline()

MassPeaks

+labelPeaks()

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Example Data

Serum Peptidome Profiling Revealed Platelet Factor 4 as aPotential Discriminating Peptide Associated withPancreatic Cancer

G.M. Fiedler, A.B. Leichtle, J. Kase et alClin Cancer Res June 1, 2009 15:3812-3819

“Two significant peaks ( m/z 3884; 5959) achieved asensitivity of 86.3% and a specificity of 97.6% for thediscrimination of patients and healthy controls . . . ”

“MALDI-TOF MS-based serum peptidome profilingallowed the discovery and validation ofplatelet factor 4 [m/z 3884, 7767; S.G.] as a newdiscriminating marker in pancreatic cancer.”

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MALDIquanthands-on: File Import

> library("MALDIquant")

> library("readBrukerFlexData")

> spectra <- mqReadBrukerFlex("/data/fiedler2009/")

> length(spectra)

[1] 480

> spectra[[1]]

S4 class type : MassSpectrum

Number of m/z values : 42388

Range of m/z values : 1000.015 - 9999.734

Range of intensity values: 5 - 101840

File : /data/fiedler2009/[...]/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid

other possibilities:

library("MALDIquant")

library("readMzXmlData")

s <- mqReadMzXml("/data/exampleMS/spectrum.mzXML")

library("MALDIquant")

s <- createMassSpectrum(mass=1:5,

intensity=runif(5),

metaData=list(name="example"))

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MALDIquanthands-on: plot

> plot(spectra[[1]])

> abline(v=3884, col="blue")

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MALDIquanthands-on: plot m/z 3884

> plot(spectra[[1]], xlim=c(3800, 4500))

> abline(v=3884, col="blue")

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MALDIquanthands-on: Variance Stabilization and Smoothing

> spectra <- lapply(spectra, transformIntensity, fun=sqrt)

> movAvg <- function(y) {return(filter(y, rep(1, 5)/5, sides=2));}

> spectra <- lapply(spectra, transformIntensity, fun=movAvg)

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MALDIquanthands-on: Baseline Correction – don’t harm the data

> bl <- estimateBaseline(spectra[[1]], method="Median")

> plot(spectra[[1]], xlim=c(3800, 4500));

> lines(bl, col="red");

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MALDIquanthands-on: Baseline Correction – SNIP

> bl <- estimateBaseline(spectra[[1]], method="SNIP")

> plot(spectra[[1]], xlim=c(3800, 4500)); lines(bl, col="red");

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C. G. Ryan, E. Clayton, W. L. Griffin, S. H. Sie, and D. R. Cousens. SNIP, a statistics-sensitive background treatment for thequantitative analysis of PIXE spectra in geoscience applications. Nucl. Instrument. Meth. B, 34:396–402, 1988

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MALDIquanthands-on: Baseline Correction – SNIP

> spectra <- lapply(spectra, removeBaseline)

> lines(spectra[[1]], col="blue")

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C. G. Ryan, E. Clayton, W. L. Griffin, S. H. Sie, and D. R. Cousens. SNIP, a statistics-sensitive background treatment for thequantitative analysis of PIXE spectra in geoscience applications. Nucl. Instrument. Meth. B, 34:396–402, 1988

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MALDIquanthands-on: Baseline Correction – SNIP

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MALDIquanthands-on: Peak Detection

> spectra[[1]]

S4 class type : MassSpectrum

Number of m/z values : 42388

Range of m/z values : 1000.015 - 9999.734

Range of intensity values: 0 - 709.207

File : /data/fiedler2009/[...]/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid

> peaks <- lapply(spectra, detectPeaks)

> peaks[[1]]

S4 class type : MassPeaks

Number of m/z values : 198

Range of m/z values : 1011.059 - 9423.422

Range of intensity values: 19.273 - 709.207

File : /data/fiedler2009/[...]/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid

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MALDIquanthands-on: Peak Detection

> plot(spectra[[1]], xlim=c(3800, 4500)); points(peaks[[1]], col="green")

> top5 <- intensity(p) %in% sort(intensity(p)[mass(p)>3800 & mass(p)<4500], decreasing=TRUE)[1:5]

> labelPeaks(peaks[[1]], index=top5); labelPeaks(peak[[1]], mass=3884, col="blue")

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4052.944090.24193.806

4209.072

4265.5643882.052

accepted maximarejected maximanoise threshold

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MALDIquantSingle Spectrum Workflow

> library("MALDIquant")

> library("readBrukerFlexData")

> spectra <- mqReadBrukerFlex("/data/fiedler2009/")

> movAvg <- function(y) {return(filter(y, rep(1, 5)/5, sides=2));}

> spectra <- lapply(spectra, transformIntensity, fun=sqrt)

> spectra <- lapply(spectra, transformIntensity, fun=movAvg)

> spectra <- lapply(spectra, removeBaseline)

> peaks <- lapply(spectra, detectPeaks)

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MALDIquantComparison of Multiple Spectra

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4052.94

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3880.425

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4088.301

spectrum 1 (control)spectrum 401 (cancer)

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MALDIquantConclusion

MALDIquant is free software (GPLv3).

Currently available:

• Easy to use and full established single spectrum workflow.

• Easy to customize.

• Easy data exchange.

Shortly available:

• Peak alignment.

• Calibration/Normalization routines.

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MALDIquantThanks

Alexander B. Leichtle, helpful discussions(Institute for Clinical Chemistry, Bern University Hospital)

Korbinian Strimmer, supervision(IMISE, University of Leipzig)

Thanks for your attention!

Download of MALDIquant software:http://strimmerlab.org/software/maldiquant/

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