Metabolomics: an interpreting tool to    understand kidney graft recipients  grouping and their recovery trajectory       ...
KidneyKidney removes wastes andfluid from the bodyItalian BioR DayLodi 30 Novembre 2012   1/36
AimKidney graft recipientsmonitoring                           immunological rejection                        ischemia/rep...
How      Urine samples +      1H-NMR            +      Chemometrics =      MetabolomicsItalian BioR DayLodi 30 Novembre 20...
MetabolomicsDetermination of multiple metabolites in biofluidsand tissues and their changes over timeInstrumentation: NMR, ...
ChemometricsChemometrics: chemistry + statistic + informaticItalian BioR DayLodi 30 Novembre 2012   5/36
Exploration + Modelling                        PCA                                PLSItalian BioR DayLodi 30 Novembre 2012...
Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012   6/36
Data ImportMatlab                    save datiperR.mat data asseX -v6                              require(R.matlab)      ...
Data Analysis       Preprocessing       and       pretreatment                               Exploration                  ...
Preprocessing + Pretreatment                        baseline and phase correction,                        referencing to i...
Preprocessing + Pretreatmentptw, dtw, xcms -> MS or LC spectraPROcess -> protein mass spectrometrypeak peakingbinning, TMS...
Preprocessing + PretreatmentTMS peaks before and after alignment                                ...with icoshift...Italian...
Preprocessing + Pretreatmentbinning# 10 points binningsynew <- colMeans(matrix(dati[1,1:32760], nrow = 10))xnew <- colMean...
Preprocessing + PretreatmentbinningItalian BioR DayLodi 30 Novembre 2012   13/36
Preprocessing + PretreatmentBaseline correctionrequire(ptw)# baseline driftybase <- 45600*asseX + 20000*asseX^2 - 2000*ass...
Preprocessing + PretreatmentBaseline correctionItalian BioR DayLodi 30 Novembre 2012   15/36
Preprocessing + PretreatmentData reduction                        p1                                               p3     ...
Preprocessing + PretreatmentData reduction     plot(dati[1,], type=l)     require (TeachingDemos)     plot(dati[1,], type=...
Preprocessing + PretreatmentUnit area normalization dati.un <- sweep(dati.r, MARGIN = 1, apply(dati, 1,function(x) sqrt(su...
Preprocessing + PretreatmentUnit area normalizationItalian BioR DayLodi 30 Novembre 2012   19/36
Preprocessing + PretreatmentScaling                              CENTERING                             AUTOSCALING        ...
Preprocessing + PretreatmentScaling require(pls) dati.c <- scale(dati.r, center = T, scale =F) dati.sc <- scale(dati.r, ce...
Preprocessing + PretreatmentScalingItalian BioR DayLodi 30 Novembre 2012   22/36
Exploration + Modelling    15 patients were sampled all along the     hospital recovery period (from 5 to 40     days) and...
Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012   24/36
Exploration + ModellingPCAPareto scaling3 patients                        Follow-up                                       ...
Exploration + ModellingPCAPareto scaling15 patients                              Follow-up                                ...
Exploration + Modelling require(ChemometricsWithR) mod <- PCA(dati.p) figura1a <- scores(mod) plot(figura1a[,2], figura1a[...
Exploration + ModellingPLS-DA analysisone class vs one class                        Modelled class:           1    2      ...
Exploration + ModellingPost operation stage.The creatinine and creatine signals arerather low and there is a lipoprotein s...
Exploration + ModellingPre discharge stage.Creatinine and creatine signals pretty highand lipoprotein signal almost disapp...
Exploration + ModellingFollow up stage.The creatinine and creatine signals are veryintenseItalian BioR DayLodi 30 Novembre...
Exploration + Modelling mod <- do.pls(dati.p, class.p, 2, scale="mean") VIP.scores.Y1 <- mod$VIP[,2] plot(-axis.scale,VIP....
Exploration + Modelling require(pls) // do.pls short command: mod <- do.pls(x,y) extensive command: mod <- do.pls(x,y, nco...
Exploration + Modelling Input: • x is the predictor matrix • y is the response variable • ncomp it is the requested number...
Exploration + Modelling Output: it is a list. • modello, it is the usual mvr output • performance, explained variance for ...
Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012   36/36
grazie per   l’attenzione    Marco Calderisi, PhD    m.calderisi@kode-solutions.net    kode-solutions.netItalian BioR DayL...
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Metabolomics: an interpreting tool to understand kidney graft recipients grouping and their recovery trajectory

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Presentazione tenuta il 30 Novembre 2012 al Parco Tecnologico Padano di Lodi in occasione del primo Italian BioR Day

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Metabolomics: an interpreting tool to understand kidney graft recipients grouping and their recovery trajectory

  1. 1. Metabolomics: an interpreting tool to understand kidney graft recipients grouping and their recovery trajectory Marco Calderisi, PhD m.calderisi@kode-solutions.net Italian BioR Day Lodi 30 Novembre 2012Italian BioR DayLodi 30 Novembre 2012
  2. 2. KidneyKidney removes wastes andfluid from the bodyItalian BioR DayLodi 30 Novembre 2012 1/36
  3. 3. AimKidney graft recipientsmonitoring immunological rejection ischemia/reperfusion injury immunosuppressant nephrotoxicityItalian BioR DayLodi 30 Novembre 2012 2/36
  4. 4. How Urine samples + 1H-NMR + Chemometrics = MetabolomicsItalian BioR DayLodi 30 Novembre 2012 3/36
  5. 5. MetabolomicsDetermination of multiple metabolites in biofluidsand tissues and their changes over timeInstrumentation: NMR, MS, GC/MS, HPLC/MSItalian BioR DayLodi 30 Novembre 2012 4/36
  6. 6. ChemometricsChemometrics: chemistry + statistic + informaticItalian BioR DayLodi 30 Novembre 2012 5/36
  7. 7. Exploration + Modelling PCA PLSItalian BioR DayLodi 30 Novembre 2012 6/36
  8. 8. Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012 6/36
  9. 9. Data ImportMatlab save datiperR.mat data asseX -v6 require(R.matlab) aa <- readMat(datiperR.mat) dati <- aa$data dati <- t(dati) asseX <- aa$asseXItalian BioR DayLodi 30 Novembre 2012 7/36
  10. 10. Data Analysis Preprocessing and pretreatment Exploration ModellingItalian BioR DayLodi 30 Novembre 2012 8/36
  11. 11. Preprocessing + Pretreatment baseline and phase correction, referencing to internal standard, signals alignment normalization centering scaling other transformationsItalian BioR DayLodi 30 Novembre 2012 9/36
  12. 12. Preprocessing + Pretreatmentptw, dtw, xcms -> MS or LC spectraPROcess -> protein mass spectrometrypeak peakingbinning, TMS alignmentItalian BioR DayLodi 30 Novembre 2012 10/36
  13. 13. Preprocessing + PretreatmentTMS peaks before and after alignment ...with icoshift...Italian BioR DayLodi 30 Novembre 2012 11/36
  14. 14. Preprocessing + Pretreatmentbinning# 10 points binningsynew <- colMeans(matrix(dati[1,1:32760], nrow = 10))xnew <- colMeans(matrix(asseX[1:32760], nrow = 10))# plotplot(asseX[1:32760], dati[1, 1:32760], type = "l", xlab = "asseX ", ylab = "response", main = "binning", col = "red", ylim=c(0,10^8))lines(xnew, ynew)Italian BioR DayLodi 30 Novembre 2012 12/36
  15. 15. Preprocessing + PretreatmentbinningItalian BioR DayLodi 30 Novembre 2012 13/36
  16. 16. Preprocessing + PretreatmentBaseline correctionrequire(ptw)# baseline driftybase <- 45600*asseX + 20000*asseX^2 - 2000*asseX^3plot(asseX, ybase[1,], type=l, col=blue)y1mod <- dati[1,]+ybase[1,]# plotplot(asseX, y1mod, type=l, ylim=c(-3000000, 10000000), col=blue)lines(asseX, dati[1,], col=red)# correctionlines(asseX, baseline.corr(y1mod), col=grey)abline(h=0, col=green, lty=2)legend(topright, legend=c(modified, original, baselinecorrected), lty=1, bty=n, col=c(blue, red, grey), cex=0.7)Italian BioR DayLodi 30 Novembre 2012 14/36
  17. 17. Preprocessing + PretreatmentBaseline correctionItalian BioR DayLodi 30 Novembre 2012 15/36
  18. 18. Preprocessing + PretreatmentData reduction p1 p3 p2 p4 δ ppm!xnew <- c(x[p1:p2,], x[p3:p4,])Italian BioR DayLodi 30 Novembre 2012 16/36
  19. 19. Preprocessing + PretreatmentData reduction plot(dati[1,], type=l) require (TeachingDemos) plot(dati[1,], type=l) # zooming the plot zoomplot(locator(2)) # find the points p1 <- locator(1) p2 <- locator(1) plot(dati[1,], type=l) # zooming the plot zoomplot(locator(2)) p3 <- locator(1) p4 <- locator(1) # new reduced dataset dati.r <- dati[,c(p1$x:p2$x, p3$x:p4$x)]Italian BioR DayLodi 30 Novembre 2012 17/36
  20. 20. Preprocessing + PretreatmentUnit area normalization dati.un <- sweep(dati.r, MARGIN = 1, apply(dati, 1,function(x) sqrt(sum(x^2))), FUN = /) par(mfrow=c(2,1)) asseX <- t(asseX.r) matplot(asseX.r, t(dati.r), type=l, lwd=1, col=setcol, lty=1) matplot(asseX.r, t(dati.un), type=l, lwd=1, col=setcol, lty=1)Italian BioR DayLodi 30 Novembre 2012 18/36
  21. 21. Preprocessing + PretreatmentUnit area normalizationItalian BioR DayLodi 30 Novembre 2012 19/36
  22. 22. Preprocessing + PretreatmentScaling CENTERING AUTOSCALING PARETO SCALING (X-Xmedio)/radq(sd)Italian BioR DayLodi 30 Novembre 2012 20/36
  23. 23. Preprocessing + PretreatmentScaling require(pls) dati.c <- scale(dati.r, center = T, scale =F) dati.sc <- scale(dati.r, center = T, scale =T) dati.p <- sweep(dati.r, MARGIN = 2, sqrt(apply(dati.r, MARGIN = 2, sd)), FUN = "/")Italian BioR DayLodi 30 Novembre 2012 21/36
  24. 24. Preprocessing + PretreatmentScalingItalian BioR DayLodi 30 Novembre 2012 22/36
  25. 25. Exploration + Modelling 15 patients were sampled all along the hospital recovery period (from 5 to 40 days) and during the first follow up systematic sampling (one sample a day)Italian BioR DayLodi 30 Novembre 2012 23/36
  26. 26. Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012 24/36
  27. 27. Exploration + ModellingPCAPareto scaling3 patients Follow-up Post-operation Pre-dischargeItalian BioR DayLodi 30 Novembre 2012 25/36
  28. 28. Exploration + ModellingPCAPareto scaling15 patients Follow-up Post-operation Pre-dischargeItalian BioR DayLodi 30 Novembre 2012 26/36
  29. 29. Exploration + Modelling require(ChemometricsWithR) mod <- PCA(dati.p) figura1a <- scores(mod) plot(figura1a[,2], figura1a[,3], xlab =PC 1 (exp. var. 13.96%), ylab = PC 2 (exp. var. 9.88%), xaxt="n", yaxt="n", mgp=c(2.5, 1, 0), cex.lab = 1, type = n) axis(1, at=seq(-1,1,0.25), lwd = 0, lwd.ticks = 1, tcl = 0.2, cex.axis=1, mgp=c(1.5, 0.5, 0)) axis(2, at=seq(-1,1,0.25), lwd = 0, lwd.ticks = 1, tcl = 0.2, cex.axis=1, las = 2, mgp=c(1.5, 0.5, 0)) points(figura1a[,2], figura1a[,3], pch=as.numeric(figura1a[,1]), cex=0.8) abline(v=0, h=0, col=grey, lty = 2)Italian BioR DayLodi 30 Novembre 2012 27/36
  30. 30. Exploration + ModellingPLS-DA analysisone class vs one class Modelled class: 1 2 3Calibration Sensitivity 0.96 0.88 0.91 Specificity 1 0.9 0.96 Class. Err 0.02 0.11 0.07Cross Validation Sensitivity 0.94 0.86 0.91 Specificity 1 0.83 0.92 Class. Err 0.03 0.15 0.08Italian BioR DayLodi 30 Novembre 2012 28/36
  31. 31. Exploration + ModellingPost operation stage.The creatinine and creatine signals arerather low and there is a lipoprotein signalItalian BioR DayLodi 30 Novembre 2012 29/36
  32. 32. Exploration + ModellingPre discharge stage.Creatinine and creatine signals pretty highand lipoprotein signal almost disappearedItalian BioR DayLodi 30 Novembre 2012 30/36
  33. 33. Exploration + ModellingFollow up stage.The creatinine and creatine signals are veryintenseItalian BioR DayLodi 30 Novembre 2012 31/36
  34. 34. Exploration + Modelling mod <- do.pls(dati.p, class.p, 2, scale="mean") VIP.scores.Y1 <- mod$VIP[,2] plot(-axis.scale,VIP.scores.Y1, type=l, xaxt=n, yaxt=n, xlab=[ppm], ylab=VIP scores, ylim=c(0,350), lwd = 0.5, cex.lab=0.8, main = VIP scores class 1, cex.main=0.9, col=black, mgp=c(2.5, 1, 0)) axis(1, at=seq(0,-10,-1), labels = seq(0,10,1), lwd = 0, lwd.ticks = 1, tcl = 0.2, cex.axis=0.8) axis(2, at=seq(0, 320, 50), lwd = 0, lwd.ticks = 1, tcl = 0.2, cex.axis=0.8, las= 1) grid(col = "lightgray", lty = "dotted", lwd =1)Italian BioR DayLodi 30 Novembre 2012 32/36
  35. 35. Exploration + Modelling require(pls) // do.pls short command: mod <- do.pls(x,y) extensive command: mod <- do.pls(x,y, ncomp, scale=c("mean", "autoscaling"), graph=c("line", "points")) aim: to do a pls regression analysis with just one “click” http://cran.r-project.org/web/packages/pls/ http://mevik.net/work/software/pls.htmlItalian BioR DayLodi 30 Novembre 2012 33/36
  36. 36. Exploration + Modelling Input: • x is the predictor matrix • y is the response variable • ncomp it is the requested number of latent variables (optional). The default is the LV’s number corresponding to the lowest RMSECV(*) • scale: choose between mean centering (default) and autoscaling • graph it is the plot layout: choose between points or line for predictors plot (optional). (*) only Leave-One-Out CV procedure it is implementedItalian BioR DayLodi 30 Novembre 2012 34/36
  37. 37. Exploration + Modelling Output: it is a list. • modello, it is the usual mvr output • performance, explained variance for predictors and response variable, RMSE, RMSECV, number of latent variables selected) • VIP • coefficientsItalian BioR DayLodi 30 Novembre 2012 35/36
  38. 38. Exploration + ModellingItalian BioR DayLodi 30 Novembre 2012 36/36
  39. 39. grazie per l’attenzione Marco Calderisi, PhD m.calderisi@kode-solutions.net kode-solutions.netItalian BioR DayLodi 30 Novembre 2012

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