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

Metabolomics: an interpreting tool to understand kidney graft recipients

grouping and their recovery trajectory

Italian BioR DayLodi 30 Novembre 2012

Marco Calderisi, [email protected]


mailto:[email protected]


Kidney


Kidney removes wastes and fluid from the body

1/36


Kidney graft recipients monitoring

Aim

immunological rejection ischemia/reperfusion injury

immunosuppressant nephrotoxicity2/36

How


Urine samples + 1H-NMR + Chemometrics = Metabolomics

3/36

Metabolomics


Determination of multiple metabolites in biofluids

and tissues and their changes over time

Instrumentation: NMR, MS, GC/MS, HPLC/MS

4/36

Chemometrics


Chemometrics: chemistry + statistic + informatic

5/36

Exploration + Modelling


PCA

PLS

6/36


Italian BioR DayLodi 30 Novembre 2012 6/36

Data Import


Matlab

save datiperR.mat data asseX '-v6'

require(R.matlab)aa <- readMat('datiperR.mat')

dati <- aa$datadati <- t(dati)

asseX <- aa$asseX

7/36

Data Analysis


Preprocessing and pretreatment

Exploration

Modelling

8/36

Preprocessing + Pretreatment


baseline and phase correction,referencing to internal standard,signals alignment

normalizationcentering

scalingother transformations

9/36



ptw, dtw, xcms -> MS or LC spectraPROcess -> protein mass spectrometry

peak peaking

binning, TMS alignment

10/36



TMS peaks before and after alignment...with icoshift...

11/36



# 10 points binnings ynew <- 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)

binning

12/36



binning

13/36



require(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', 'baseline corrected'), lty=1, bty='n', col=c('blue', 'red', 'grey'), cex=0.7)

Baseline correction

14/36



Baseline correction

15/36



δ ppm!

Data reduction

xnew <- c(x[p1:p2,], x[p3:p4,])

p1

p2

p3

p4

16/36



Data reduction

plot(dati[1,], type='l')require (TeachingDemos)plot(dati[1,], type='l')# zooming the plotzoomplot(locator(2))# find the pointsp1 <- locator(1)p2 <- locator(1)plot(dati[1,], type='l')# zooming the plotzoomplot(locator(2))p3 <- locator(1)p4 <- locator(1)# new reduced datasetdati.r <- dati[,c(p1$x:p2$x, p3$x:p4$x)]

17/36



Unit area normalizationdati.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)

18/36



Unit area normalization

19/36



ScalingCENTERING

AUTOSCALING

PARETO SCALING

(X-Xmedio)/radq(sd)

20/36



Scalingrequire(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 = "/")

21/36



Scaling

22/36



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)

23/36



PCAPareto scaling3 patients

Post-operation

Pre-discharge

Follow-up

25/36



PCAPareto scaling15 patients

Pre-discharge

Post-operation

Follow-up

26/36



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)

27/36



PLS-DA analysisone class vs one class

Modelled class: 1 2 3

Calibration Sensitivity 0.96 0.88 0.91

Specificity 1 0.9 0.96

Class. Err 0.02 0.11 0.07

Cross Validation Sensitivity 0.94 0.86 0.91

Specificity 1 0.83 0.92

Class. Err 0.03 0.15 0.08

28/36



Post operation stage.The creatinine and creatine signals are rather low and there is a lipoprotein signal

29/36



Pre discharge stage.Creatinine and creatine signals pretty high and lipoprotein signal almost disappeared

30/36



Follow up stage.The creatinine and creatine signals are very intense

31/36



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)

32/36



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.html

33/36

http://cran.r-project.org/web/packages/pls/

http://cran.r-project.org/web/packages/pls/

http://mevik.net/work/software/pls.html

http://mevik.net/work/software/pls.html



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 implemented

34/36



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

•coefficients

35/36

grazie per l’attenzioneMarco Calderisi, [email protected]




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

Documents

Transcript of Metabolomics: an interpreting tool to understand kidney graft recipients grouping and their recovery...