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

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

Marco Calderisi, PhD
m.calderisi@kode-solutions.net

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Lodi 30 Novembre 2012
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Kidney

Kidney removes wastes and
ﬂuid from the body

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Lodi 30 Novembre 2012 1/36

Aim
Kidney graft recipients
monitoring

immunological rejection
ischemia/reperfusion injury
immunosuppressant nephrotoxicity
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How

Urine samples +
1H-NMR +
Chemometrics =
Metabolomics

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Metabolomics

Determination of multiple metabolites in bioﬂuids

and tissues and their changes over time

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

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Chemometrics
Chemometrics: chemistry + statistic + informatic

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Exploration + Modelling
PCA

PLS

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Data Import
Matlab

save datiperR.mat data asseX '-v6'

require(R.matlab)
aa <- readMat('datiperR.mat')
dati <- aa$data
dati <- t(dati)
asseX <- aa$asseX

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Data Analysis

Preprocessing
and
pretreatment

Exploration

Modelling

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Preprocessing + Pretreatment

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

normalization
centering
scaling
other transformations

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

peak peaking

binning, TMS alignment

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

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binning
# 10 points binnings
ynew <- colMeans(matrix(dati[1,1:32760], nrow = 10))
xnew <- colMeans(matrix(asseX[1:32760], nrow = 10))

# plot
plot(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)

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binning

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Baseline correction
require(ptw)
# baseline drift
ybase <- 45600*asseX + 20000*asseX^2 - 2000*asseX^3
plot(asseX, ybase[1,], type='l', col='blue')
y1mod <- dati[1,]+ybase[1,]
# plot
plot(asseX, y1mod, type='l', ylim=c(-3000000, 10000000), col='blue')
lines(asseX, dati[1,], col='red')
# correction
lines(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)

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Baseline correction

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Data reduction

p1
p3

p2 p4

δ ppm!

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

plot(dati[1,], type='l')
require (TeachingDemos)
# zooming the plot
zoomplot(locator(2))
# find the points
p1 <- locator(1)
p2 <- locator(1)
# 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)]

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Unit 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)

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Unit area normalization

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Scaling
CENTERING

AUTOSCALING

PARETO SCALING

(X-Xmedio)/radq(sd)

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Scaling
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 = "/")

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Scaling

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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)

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PCA
Pareto scaling
3 patients
Follow-up

Post-operation

Pre-discharge

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PCA
Pareto scaling
15 patients
Follow-up

Post-operation

Pre-discharge

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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)
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PLS-DA analysis
one 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

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

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

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

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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)

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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

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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

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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

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grazie per
l’attenzione
Marco Calderisi, PhD
m.calderisi@kode-solutions.net
kode-solutions.net

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

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