Intelligent Bucketing for Metabonomics—Part 2
Brent Lefebvre, Ryan Sasaki, and Sergey Golotvin
Advanced Chemistry Development, Inc.,Toronto, Ontario M5H 3V9
Andrew NichollsGlaxoSmithKline,
Ware, UK
INTRODUCTION
Auto processing using ACD/Labs’ phasing andbaseline correction is now the standard approachfor Metabonomics studies of urine. These toolshave been proven time and time again to offerthe most accurate results and produce the beststatistical models for these types of data sets.Now a new procedure for Metabonomics studies,called “Intelligent Bucketing”, is being introducedto further enhance the benefits this productprovides to the metabolic profiling industry.
In the first poster of this series, “IntelligentBucketing for Metabonomics-Part 1”1, it wasshown that Intelligent Bucketing was designed tomake smart bucket divisions in complex spectrasuch as those seen in Metabonomics. In Part 2 ofthis series, we will attempt to show how the resultof placing these integral regions in smarterlocations leads to better statistical modeling.
Figure 1 - TCA cycle intermediates. Notice theshifting of these components. This is typical inurine spectra and the peak movement is notalways controllable by pH adjustment. Divalentcation concentration and temperature will alsoaffect these shifts.
METHODS
The data used here are from two previous studies.The first set is from a published study2 ofacclimation to a normal environment for germ-free rats, and is used to show the effect on thevariance explained within a model and theinterpretation of the variable loadings. Thesecond set of data, used to show the modelquality for the PCA, is from a study on drug-induced kidney effects.
Both data sets were automatically processed withACD/1D NMR Processor, with an average time of5s from raw data to fully processed, phased, andbase-lined spectrum. These spectra were thenloaded into the interface in “add mode”, as theyappear in Figure 1, Intelligent Bucketing wasperformed and the data were exported into a textfile for statistical analysis.
DISCUSSION
When smarter bucket divisions are made, such asthose that are optimized to ensure single peaksdo not span two bins or buckets, data can bebetter modeled with fewer principal components.Normally a PC analysis will take into account apeak that spans two buckets by putting both ofthose buckets in the same principal component.
However, there are two problems with thisapproach. First, the rest of the peaks in thebucket could be from something else that mayhave been producing an independent variable.This contribution will now be lost in the statisticalmodel.
Second, if the peak changes location betweenspectra, however slight, the contribution of thispeak between the buckets will vary and this willdecrease the accuracy of the analysis andpotentially confuse the interpretation of theresults. Intelligent Bucketing avoids these twocommon problems. It is shown below how thisroutine then leads the analyst to more accuratemodels which understand the underlyingbiochemistry.
RESULTS
In the first test of how the Intelligent Bucketingperformed against Classical Bucketing, acomparison of the % variance explained insuccessive principal components (PCs) was made.Ideally, any model obtained should be able todescribe the variation in the original data in asfew PCs as possible. In Figure 2, we can see thatthe Intelligent Bucketing yielded a 16% increasein the variation explained (R2) in PC1.
Figure 2 - The quality of the prediction model forR2 and Q2 in Classical and Intelligent Bucketing.The increased variation explained in the first fewcomponents results in the need for a smallernumber of components to model the data. Theincrease in Q2 indicates a higher predictive abilityfor comparable components.
In Figure 3, the loadings plots clearly show thebenefit of Intelligent Bucketing. On this type ofplot, the ideal situation is when all of the integralregions from a specific metabolite are clusteredvery close together. If they are not, then it showsthat other underlying metabolites are contributingto the region and complicating the interpretationof the model. Here we can see the intelligentbuckets produce more accurate clustering withimproved precision for the regions specific to themetabolites.
Figure 3 - In these loadings plots, some of theintegral regions are assigned to the metaboliteslocated in them. It is clear that IntelligentBucketing (B) produces more precise regions foreach metabolite, allowing for a clearer distinctionof the metabolic components responsible for anobserved effect.
Figure 4 - The trajectory of the time courseproduced with the intelligent buckets (A) follows asmoother course and backtracks less. This iswhat would be expected from a gradual changein metabolism following environmental exposure.
- 0 .7 0
- 0 .6 0
- 0 .5 0
- 0 .4 0
- 0 .3 0
- 0 .2 0
- 0 .1 0
0 .0 0
0 .1 0
0 .2 0
0 .3 0
0 .4 0
0 .5 0
0 .6 0
0 .7 0
-0 .9 0 - 0 .8 0 - 0 .7 0 - 0 .6 0 - 0 .5 0 - 0 .4 0 - 0 .3 0 -0 .2 0 - 0 .1 0 0 .0 0 0 .1 0 0 .2 0 0 .3 0 0 .4 0 0 .5 0 0 .6 0 0 .7 0 0 .8 0 0 .9 0
t[2]
t[1]
Ellipse: Hotelling T2 (0.95)
An 1 Day 21
An 4 24-48h
An 4 Day 9
An 4 Day 17
An 7 6-24h
An 7 Day 6 An 7 Day 12
An 7 Day 17
An 7 Day 9
An 7 24-48h An 7 Day 4
An 4 Day 21
An 7 0-6h
An 4 Day 15
An 4 Day 12
An 4 Day 4
An 4 Day 6
An 4 6-24h
An 4 0-6h
An 1 Day 17
An 1 Day 15
An 1 Day 12
An 1 Day 9 An 1 Day 6
An 1 Day 4
An 1 6-24h An 1 24-48h
An 1 0-6h
An 7 Day 21
SIMCA-P+ 10.5 - 02/09/2004 11:12:13
- 0 .6 0
- 0 .5 0
- 0 .4 0
- 0 .3 0
- 0 .2 0
- 0 .1 0
0 .0 0
0 .1 0
0 .2 0
0 .3 0
0 .4 0
0 .5 0
0 .6 0
- 1 .0 0 - 0 .9 0 - 0 .8 0 - 0 .7 0 - 0 .6 0 - 0 .5 0 - 0 .4 0 - 0 .3 0 - 0 .2 0 - 0 .1 0 0 .0 0 0 .1 0 0 .2 0 0 .3 0 0 .4 0 0 .5 0 0 .6 0 0 .7 0 0 .8 0 0 .9 0 1 .0 0
t[2]
t[1]
Ellipse: Hotelling T2 (0.95)
An 1 Day 21
An 4 24-48h
An 4 Day 9
An 4 Day 17
An 7 6-24h
An 7 Day 6
An 7 Day 12
An 7 Day 17
An 7 Day 9
An 7 24-48h An 7 Day 4
An 4 Day 21
An 7 0-6h
An 4 Day 15
An 4 Day 12
An 4 Day 4
An 4 Day 6
An 4 6-24h
An 4 0-6h
An 1 Day 17
An 1 Day 15
An 1 Day 12
An 1 Day 9
An 1 Day 6
An 1 Day 4
An 1 6-24h An 1 24-48h
An 1 0-6h
An 7 Day 21
SIMCA-P+ 10.5 - 02/09/2004 11:12:45
- 0 .3 0
- 0 .2 0
- 0 .1 0
0 .0 0
0 .1 0
0 .2 0
- 0 .2 0 - 0 .1 0 0 .0 0 0 .1 0 0 .2 0 0 .3 0
p[2]
p[1]
[-1.19 .. -1.17][-1.17 .. -1.14][-1.14 .. -1.12][-1.12 .. -1.10][-1.10 .. -1.08][-1.08 .. -1.07][-1.07 .. -1.05][-1.05 .. -1.03][-1.03 .. -1.01][-1.01 .. -0.98][-0.98 .. -0.97][-0.97 .. -0.95][-0.95 .. -0.93][-0.93 .. -0.92][-0.92 .. -0.89][-0.89 .. -0.87][-0.87 .. -0.84][-0.84 .. -0.81][-0.81 .. -0.80][-0.80 .. -0.77][-0.77 .. -0.75][-0.75 .. -0.72][-0.72 .. -0.69][-0.69 .. -0.68][-0.68 .. -0.67][-0.67 .. -0.66][-0.66 .. -0.65][-0.65 .. -0.62][-0.62 .. -0.61][-0.61 .. -0.58][-0.58 .. -0.56][-0.56 .. -0.54][-0.54 .. -0.52][-0.52 .. -0.51][-0.51 .. -0.49][-0.49 .. -0.48][-0.48 .. -0.45][-0.45 .. -0.43][-0.43 .. -0.41][-0.41 .. -0.39][-0.39 .. -0.37][-0.37 .. -0.36][-0.36 .. -0.34][-0.34 .. -0.33][-0.33 .. -0.30][-0.30 .. -0.27][-0.27 .. -0.26][-0.26 .. -0.23][-0.23 .. -0.22][-0.22 .. -0.21][-0.21 .. -0.20][-0.20 .. -0.19][-0.19 .. -0.17][-0.17 .. -0.16][-0.16 .. -0.14][-0.14 .. -0.13][-0.13 .. -0.11][-0.11 .. -0.08][-0.08 .. -0.07][-0.07 .. -0.06][0.02 .. 0.05][0.05 .. 0.06][0.06 .. 0.07][0.07 .. 0.08][0.08 .. 0.10][0.10 .. 0.11][0.11 .. 0.13][0.13 .. 0.14][0.14 .. 0.16][0.16 .. 0.18][0.18 .. 0.19][0.19 .. 0.21][0.21 .. 0.24][0.24 .. 0.25][0.25 .. 0.26][0.26 .. 0.27][0.27 .. 0.30][0.30 .. 0.33][0.33 .. 0.36][0.36 .. 0.37][0.37 .. 0.38][0.38 .. 0.39][0.39 .. 0.40][0.40 .. 0.41][0.41 .. 0.42][0.42 .. 0.43][0.43 .. 0.44][0.44 .. 0.47][0.47 .. 0.49][0.49 .. 0.50][0.50 .. 0.51][0.51 .. 0.52][0.52 .. 0.53][0.53 .. 0.56][0.56 .. 0.57][0.57 .. 0.58][0.58 .. 0.59][0.59 .. 0.62][0.62 .. 0.63][0.63 .. 0.64][0.64 .. 0.66][0.66 .. 0.67][0.67 .. 0.68][0.68 .. 0.69][0.69 .. 0.70][0.70 .. 0.71][0.71 .. 0.72][0.72 .. 0.73][0.73 .. 0.74][0.74 .. 0.75]
[0.75 .. 0.78][0.78 .. 0.79][0.79 .. 0.80][0.80 .. 0.81][0.81 .. 0.82][0.82 .. 0.83][0.83 .. 0.84][0.84 .. 0.85][0.85 .. 0.86][0.86 .. 0.87]
[0.87 .. 0.89]
[0.89 .. 0.92][0.92 .. 0.94]
[0.94 .. 0.97]
[0.97 .. 1.00][1.00 .. 1.03][1.03 .. 1.04][1.04 .. 1.05][1.05 .. 1.08]
[1.08 .. 1.09][1.09 .. 1.10]
[1.10 .. 1.13][1.13 .. 1.15][1.15 .. 1.16][1.16 .. 1.18][1.18 .. 1.19][1.19 .. 1.20][1.20 .. 1.21][1.21 .. 1.23][1.23 .. 1.26]
[1.26 .. 1.29][1.29 .. 1.30]
[1.30 .. 1.32][1.32 .. 1.33]
[1.33 .. 1.36]
[1.36 .. 1.38][1.38 .. 1.40][1.40 .. 1.41]
[1.41 .. 1.43][1.43 .. 1.45][1.45 .. 1.46][1.46 .. 1.47]
[1.47 .. 1.49][1.49 .. 1.51][1.51 .. 1.52][1.52 .. 1.53][1.53 .. 1.54]
[1.54 .. 1.57][1.57 .. 1.60][1.60 .. 1.62]
[1.62 .. 1.63][1.63 .. 1.64][1.64 .. 1.67][1.67 .. 1.70]
[1.70 .. 1.71]
[1.71 .. 1.73]
[1.73 .. 1.74]
[1.74 .. 1.75]
[1.75 .. 1.76]
[1.76 .. 1.79]
[1.79 .. 1.81]
[1.81 .. 1.84][1.84 .. 1.86]
[1.86 .. 1.87][1.87 .. 1.88]
[1.88 .. 1.89][1.89 .. 1.90]
[1.90 .. 1.93][1.93 .. 1.96]
[1.96 .. 1.97][1.97 .. 1.98]
[1.98 .. 2.00][2.00 .. 2.01][2.01 .. 2.02]
[2.02 .. 2.05]
[2.05 .. 2.08][2.08 .. 2.11][2.11 .. 2.13]
[2.13 .. 2.16][2.16 .. 2.18][2.18 .. 2.19][2.19 .. 2.20]
[2.20 .. 2.21][2.21 .. 2.23]
[2.23 .. 2.26][2.26 .. 2.29]
[2.29 .. 2.30][2.30 .. 2.31]
[2.31 .. 2.33]
[2.33 .. 2.35]
[2.35 .. 2.37][2.37 .. 2.40]
[2.40 .. 2.41]
[2.41 .. 2.42]
[2.42 .. 2.43]
[2.43 .. 2.45]
[2.45 .. 2.47]
[2.47 .. 2.50]
[2.50 .. 2.52][2.52 .. 2.53]
[2.53 .. 2.54][2.54 .. 2.57][2.57 .. 2.60]
[2.60 .. 2.63]
[2.63 .. 2.64][2.64 .. 2.66][2.66 .. 2.67][2.67 .. 2.68][2.68 .. 2.69]
[2.69 .. 2.72]
[2.72 .. 2.75]
[2.75 .. 2.78]
[2.78 .. 2.81]
[2.81 .. 2.82]
[2.82 .. 2.85]
[2.85 .. 2.88]
[2.88 .. 2.89]
[2.89 .. 2.90]
[2.90 .. 2.92]
[2.92 .. 2.95]
[2.95 .. 2.96][2.96 .. 2.98][2.98 .. 2.99][2.99 .. 3.00]
[3.00 .. 3.02]
[3.02 .. 3.05]
[3.05 .. 3.08]
[3.08 .. 3.10][3.10 .. 3.12][3.12 .. 3.14]
[3.14 .. 3.15]
[3.15 .. 3.18]
[3.18 .. 3.19]
[3.19 .. 3.22]
[3.22 .. 3.23]
[3.23 .. 3.25][3.25 .. 3.27]
[3.27 .. 3.30]
[3.30 .. 3.33]
[3.33 .. 3.36]
[3.36 .. 3.37]
[3.37 .. 3.38][3.38 .. 3.39]
[3.39 .. 3.40][3.40 .. 3.42]
[3.42 .. 3.45]
[3.45 .. 3.48]
[3.48 .. 3.50][3.50 .. 3.52][3.52 .. 3.55]
[3.55 .. 3.57]
[3.57 .. 3.59]
[3.59 .. 3.61]
[3.61 .. 3.62][3.62 .. 3.63]
[3.63 .. 3.64]
[3.64 .. 3.67][3.67 .. 3.69]
[3.69 .. 3.70]
[3.70 .. 3.71][3.71 .. 3.74]
[3.74 .. 3.75]
[3.75 .. 3.78][3.78 .. 3.81]
[3.81 .. 3.82][3.82 .. 3.83][3.83 .. 3.86]
[3.86 .. 3.88]
[3.88 .. 3.91]
[3.91 .. 3.94]
[3.94 .. 3.96]
[3.96 .. 3.99]
[3.99 .. 4.01]
[4.01 .. 4.04]
[4.04 .. 4.05][4.05 .. 4.07][4.07 .. 4.08]
[4.08 .. 4.11]
[4.11 .. 4.13][4.13 .. 4.16]
[4.16 .. 4.18][4.18 .. 4.21][4.21 .. 4.23]
[4.23 .. 4.26][4.26 .. 4.27][4.27 .. 4.28]
[4.28 .. 4.31][4.31 .. 4.34]
[4.34 .. 4.37][4.37 .. 4.39]
[4.39 .. 4.40][4.40 .. 4.41][4.41 .. 4.43]
[4.43 .. 4.46][4.46 .. 4.47]
[4.47 .. 4.50]
[4.50 .. 4.51][4.51 .. 4.52][4.52 .. 4.55][4.55 .. 4.56]
[4.90 .. 4.91][4.91 .. 4.94]
[4.94 .. 4.95][4.95 .. 4.96][4.96 .. 4.99]
[4.99 .. 5.02][5.02 .. 5.04]
[5.04 .. 5.05][5.05 .. 5.06][5.06 .. 5.08][5.08 .. 5.09][5.09 .. 5.10]
[5.10 .. 5.11]
[5.11 .. 5.14]
[5.14 .. 5.17]
[5.17 .. 5.18][5.18 .. 5.19][5.19 .. 5.21][5.21 .. 5.22][5.22 .. 5.23][5.23 .. 5.24][5.24 .. 5.27] [5.27 .. 5.30]
[5.30 .. 5.31][5.31 .. 5.32][5.32 .. 5.33][5.33 .. 5.34][5.34 .. 5.35][5.35 .. 5.36][5.36 .. 5.37]
[5.37 .. 5.39]
[5.39 .. 5.42]
[5.42 .. 5.45]
[5.45 .. 5.48][6.10 .. 6.12][6.12 .. 6.13][6.13 .. 6.16][6.16 .. 6.18][6.18 .. 6.21][6.21 .. 6.24][6.24 .. 6.25][6.25 .. 6.26]
[6.26 .. 6.29][6.29 .. 6.32]
[6.32 .. 6.33][6.33 .. 6.34][6.34 .. 6.35][6.35 .. 6.38][6.38 .. 6.41][6.41 .. 6.42][6.42 .. 6.43][6.43 .. 6.45][6.45 .. 6.46][6.46 .. 6.47][6.47 .. 6.48][6.48 .. 6.51][6.51 .. 6.52]
[6.52 .. 6.55]
[6.55 .. 6.56][6.56 .. 6.59]
[6.59 .. 6.60][6.60 .. 6.61][6.61 .. 6.62][6.62 .. 6.63]
[6.63 .. 6.65][6.65 .. 6.67][6.67 .. 6.69][6.69 .. 6.72][6.72 .. 6.73][6.73 .. 6.74]
[6.74 .. 6.77]
[6.77 .. 6.78]
[6.78 .. 6.81]
[6.81 .. 6.82][6.82 .. 6.83][6.83 .. 6.85]
[6.85 .. 6.86][6.86 .. 6.89]
[6.89 .. 6.92][6.92 .. 6.95][6.95 .. 6.97][6.97 .. 6.98][6.98 .. 7.01]
[7.01 .. 7.02][7.02 .. 7.03][7.03 .. 7.04]
[7.04 .. 7.07]
[7.07 .. 7.10][7.10 .. 7.11][7.11 .. 7.13][7.13 .. 7.14][7.14 .. 7.15]
[7.15 .. 7.17][7.17 .. 7.18]
[7.18 .. 7.21]
[7.21 .. 7.23][7.23 .. 7.26]
[7.26 .. 7.29]
[7.29 .. 7.31]
[7.31 .. 7.32][7.32 .. 7.33][7.33 .. 7.34][7.34 .. 7.36]
[7.36 .. 7.39][7.39 .. 7.40]
[7.40 .. 7.43][7.43 .. 7.45]
[7.45 .. 7.47][7.47 .. 7.48][7.48 .. 7.50][7.50 .. 7.52]
[7.52 .. 7.53][7.53 .. 7.56][7.56 .. 7.59][7.59 .. 7.60][7.60 .. 7.61][7.61 .. 7.62]
[7.62 .. 7.64][7.64 .. 7.65][7.65 .. 7.68]
[7.68 .. 7.71]
[7.71 .. 7.74][7.74 .. 7.76][7.76 .. 7.78][7.78 .. 7.79][7.79 .. 7.80][7.80 .. 7.81]
[7.81 .. 7.82]
[7.82 .. 7.85]
[7.85 .. 7.87][7.87 .. 7.88][7.88 .. 7.90]
[7.90 .. 7.91][7.91 .. 7.92][7.92 .. 7.93][7.93 .. 7.94][7.94 .. 7.95][7.95 .. 7.96]
[7.96 .. 7.99]
[7.99 .. 8.02]
[8.02 .. 8.04][8.04 .. 8.07][8.07 .. 8.10]
[8.10 .. 8.13][8.13 .. 8.14][8.14 .. 8.15][8.15 .. 8.16][8.16 .. 8.17][8.17 .. 8.20]
[8.20 .. 8.23]
[8.23 .. 8.24][8.24 .. 8.26][8.26 .. 8.29][8.29 .. 8.31][8.31 .. 8.34][8.34 .. 8.35][8.35 .. 8.38][8.38 .. 8.40][8.40 .. 8.41][8.41 .. 8.42][8.42 .. 8.43][8.43 .. 8.44][8.44 .. 8.45]
[8.45 .. 8.48]
[8.48 .. 8.51][8.51 .. 8.53][8.53 .. 8.56][8.56 .. 8.59][8.59 .. 8.62][8.62 .. 8.64][8.64 .. 8.67][8.67 .. 8.69][8.69 .. 8.70][8.70 .. 8.71][8.71 .. 8.73][8.73 .. 8.76][8.76 .. 8.78][8.78 .. 8.79][8.79 .. 8.80][8.80 .. 8.81]
[8.81 .. 8.82][8.82 .. 8.85][8.85 .. 8.87]
[8.87 .. 8.88][8.88 .. 8.91]
[8.91 .. 8.92]
[8.92 .. 8.93][8.93 .. 8.94]
[8.94 .. 8.97][8.97 .. 9.00][9.00 .. 9.02][9.02 .. 9.05][9.05 .. 9.06][9.06 .. 9.07][9.07 .. 9.08][9.08 .. 9.09][9.09 .. 9.10][9.10 .. 9.13][9.13 .. 9.16][9.16 .. 9.17][9.17 .. 9.19][9.19 .. 9.21][9.21 .. 9.22][9.22 .. 9.23][9.23 .. 9.24][9.24 .. 9.25][9.25 .. 9.28]
[9.28 .. 9.31]
[9.31 .. 9.34][9.34 .. 9.35][9.35 .. 9.37][9.37 .. 9.39][9.39 .. 9.40][9.40 .. 9.41][9.41 .. 9.42][9.42 .. 9.43][9.43 .. 9.46][9.46 .. 9.48][9.48 .. 9.51][9.51 .. 9.54][9.54 .. 9.57][9.57 .. 9.59][9.59 .. 9.60][9.60 .. 9.62][9.62 .. 9.64][9.64 .. 9.66][9.66 .. 9.67][9.67 .. 9.70][9.70 .. 9.72][9.72 .. 9.74][9.74 .. 9.77][9.77 .. 9.78][9.78 .. 9.79][9.79 .. 9.80][9.80 .. 9.82][9.82 .. 9.84][9.84 .. 9.87][9.87 .. 9.88][9.88 .. 9.89][9.89 .. 9.91][9.91 .. 9.93][9.93 .. 9.94]
SIMCA-P+ 10.5 - 02/09/2004 11:09:04
2-oxoglutarate
Citrate
Succinate
Citrate and 2-oxoglutarate
- 0 .2 0
- 0 .1 0
0 .0 0
0 .1 0
0 .2 0
-0 .2 0 -0 .1 0 0 .0 0 0 .1 0 0 .2 0
p[2]
p[1]
[-1.19 .. -1.17][-1.17 .. -1.15][-1.15 .. -1.13][-1.13 .. -1.11][-1.11 .. -1.09][-1.09 .. -1.07][-1.07 .. -1.05][-1.05 .. -1.03][-1.03 .. -1.01][-1.01 .. -0.99][-0.99 .. -0.97][-0.97 .. -0.95][-0.95 .. -0.93][-0.93 .. -0.91][-0.91 .. -0.89][-0.89 .. -0.87][-0.87 .. -0.85][-0.85 .. -0.83][-0.83 .. -0.81][-0.81 .. -0.79][-0.79 .. -0.77][-0.77 .. -0.75][-0.75 .. -0.73][-0.73 .. -0.71][-0.71 .. -0.69][-0.69 .. -0.67][-0.67 .. -0.65][-0.65 .. -0.63][-0.63 .. -0.61][-0.61 .. -0.59][-0.59 .. -0.57][-0.57 .. -0.55][-0.55 .. -0.53][-0.53 .. -0.51][-0.51 .. -0.49][-0.49 .. -0.47][-0.47 .. -0.45][-0.45 .. -0.43][-0.43 .. -0.41][-0.41 .. -0.39][-0.39 .. -0.37][-0.37 .. -0.35][-0.35 .. -0.33][-0.33 .. -0.31][-0.31 .. -0.29][-0.29 .. -0.27][-0.27 .. -0.25][-0.25 .. -0.23][-0.23 .. -0.21][-0.21 .. -0.19][-0.19 .. -0.17][-0.17 .. -0.15][-0.15 .. -0.13][-0.13 .. -0.11][-0.11 .. -0.09][-0.09 .. -0.07][-0.07 .. -0.05][-0.05 .. -0.03][0.02 .. 0.04][0.04 .. 0.06][0.06 .. 0.08][0.08 .. 0.10][0.10 .. 0.12][0.12 .. 0.14][0.14 .. 0.16][0.16 .. 0.18][0.18 .. 0.20][0.20 .. 0.22][0.22 .. 0.24][0.24 .. 0.26][0.26 .. 0.28][0.28 .. 0.30][0.30 .. 0.32][0.32 .. 0.34][0.34 .. 0.36][0.36 .. 0.38][0.38 .. 0.40][0.40 .. 0.42][0.42 .. 0.44][0.44 .. 0.46][0.46 .. 0.48][0.48 .. 0.50][0.50 .. 0.52][0.52 .. 0.54][0.54 .. 0.56][0.56 .. 0.58][0.58 .. 0.60][0.60 .. 0.62][0.62 .. 0.64][0.64 .. 0.66][0.66 .. 0.68][0.68 .. 0.70][0.70 .. 0.72][0.72 .. 0.74][0.74 .. 0.76][0.76 .. 0.78][0.78 .. 0.80][0.80 .. 0.82][0.82 .. 0.84][0.84 .. 0.86]
[0.86 .. 0.88]
[0.88 .. 0.90][0.90 .. 0.92]
[0.92 .. 0.94][0.94 .. 0.96]
[0.96 .. 0.98][0.98 .. 1.00]
[1.00 .. 1.02][1.02 .. 1.04][1.04 .. 1.06]
[1.06 .. 1.08]
[1.08 .. 1.10][1.10 .. 1.12][1.12 .. 1.14][1.14 .. 1.16]
[1.16 .. 1.18][1.18 .. 1.20][1.20 .. 1.22][1.22 .. 1.24][1.24 .. 1.26]
[1.26 .. 1.28][1.28 .. 1.30]
[1.30 .. 1.32][1.32 .. 1.34][1.34 .. 1.36]
[1.36 .. 1.38][1.38 .. 1.40][1.40 .. 1.42][1.42 .. 1.44][1.44 .. 1.46][1.46 .. 1.48][1.48 .. 1.50][1.50 .. 1.52]
[1.52 .. 1.54][1.54 .. 1.56]
[1.56 .. 1.58][1.58 .. 1.60][1.60 .. 1.62][1.62 .. 1.64]
[1.64 .. 1.66][1.66 .. 1.68][1.68 .. 1.70]
[1.70 .. 1.72]
[1.72 .. 1.74]
[1.74 .. 1.76][1.76 .. 1.78]
[1.78 .. 1.80]
[1.80 .. 1.82]
[1.82 .. 1.84][1.84 .. 1.86][1.86 .. 1.88]
[1.88 .. 1.90]
[1.90 .. 1.92]
[1.92 .. 1.94]
[1.94 .. 1.96]
[1.96 .. 1.98]
[1.98 .. 2.00][2.00 .. 2.02]
[2.02 .. 2.04]
[2.04 .. 2.06]
[2.06 .. 2.08]
[2.08 .. 2.10][2.10 .. 2.12][2.12 .. 2.14][2.14 .. 2.16]
[2.16 .. 2.18][2.18 .. 2.20]
[2.20 .. 2.22][2.22 .. 2.24]
[2.24 .. 2.26]
[2.26 .. 2.28][2.28 .. 2.30][2.30 .. 2.32]
[2.32 .. 2.34]
[2.34 .. 2.36]
[2.36 .. 2.38]
[2.38 .. 2.40]
[2.40 .. 2.42]
[2.42 .. 2.44]
[2.44 .. 2.46]
[2.46 .. 2.48]
[2.48 .. 2.50]
[2.50 .. 2.52]
[2.52 .. 2.54]
[2.54 .. 2.56]
[2.56 .. 2.58]
[2.58 .. 2.60]
[2.60 .. 2.62]
[2.62 .. 2.64]
[2.64 .. 2.66]
[2.66 .. 2.68]
[2.68 .. 2.70]
[2.70 .. 2.72]
[2.72 .. 2.74]
[2.74 .. 2.76]
[2.76 .. 2.78][2.78 .. 2.80]
[2.80 .. 2.82]
[2.82 .. 2.84][2.84 .. 2.86]
[2.86 .. 2.88]
[2.88 .. 2.90]
[2.90 .. 2.92][2.92 .. 2.94]
[2.94 .. 2.96][2.96 .. 2.98]
[2.98 .. 3.00]
[3.00 .. 3.02]
[3.02 .. 3.04]
[3.04 .. 3.06]
[3.06 .. 3.08]
[3.08 .. 3.10][3.10 .. 3.12][3.12 .. 3.14]
[3.14 .. 3.16][3.16 .. 3.18]
[3.18 .. 3.20][3.20 .. 3.22]
[3.22 .. 3.24]
[3.24 .. 3.26]
[3.26 .. 3.28]
[3.28 .. 3.30]
[3.30 .. 3.32]
[3.32 .. 3.34]
[3.34 .. 3.36]
[3.36 .. 3.38]
[3.38 .. 3.40]
[3.40 .. 3.42]
[3.42 .. 3.44][3.44 .. 3.46]
[3.46 .. 3.48][3.48 .. 3.50]
[3.50 .. 3.52] [3.52 .. 3.54][3.54 .. 3.56]
[3.56 .. 3.58]
[3.58 .. 3.60]
[3.60 .. 3.62]
[3.62 .. 3.64]
[3.64 .. 3.66][3.66 .. 3.68][3.68 .. 3.70]
[3.70 .. 3.72]
[3.72 .. 3.74][3.74 .. 3.76]
[3.76 .. 3.78][3.78 .. 3.80]
[3.80 .. 3.82]
[3.82 .. 3.84][3.84 .. 3.86][3.86 .. 3.88]
[3.88 .. 3.90][3.90 .. 3.92]
[3.92 .. 3.94]
[3.94 .. 3.96]
[3.96 .. 3.98]
[3.98 .. 4.00]
[4.00 .. 4.02]
[4.02 .. 4.04][4.04 .. 4.06]
[4.06 .. 4.08]
[4.08 .. 4.10]
[4.10 .. 4.12]
[4.12 .. 4.14][4.14 .. 4.16]
[4.16 .. 4.18][4.18 .. 4.20][4.20 .. 4.22][4.22 .. 4.24]
[4.24 .. 4.26][4.26 .. 4.28]
[4.28 .. 4.30][4.30 .. 4.32]
[4.32 .. 4.34][4.34 .. 4.36][4.36 .. 4.38][4.38 .. 4.40]
[4.40 .. 4.42]
[4.42 .. 4.44]
[4.44 .. 4.46]
[4.46 .. 4.48]
[4.48 .. 4.50]
[4.50 .. 4.52][4.52 .. 4.54][4.54 .. 4.56][4.56 .. 4.58]
[4.90 .. 4.92][4.92 .. 4.94][4.94 .. 4.96][4.96 .. 4.98][4.98 .. 5.00]
[5.00 .. 5.02]
[5.02 .. 5.04][5.04 .. 5.06][5.06 .. 5.08][5.08 .. 5.10]
[5.10 .. 5.12]
[5.12 .. 5.14]
[5.14 .. 5.16][5.16 .. 5.18][5.18 .. 5.20][5.20 .. 5.22][5.22 .. 5.24][5.24 .. 5.26] [5.26 .. 5.28]
[5.28 .. 5.30][5.30 .. 5.32][5.32 .. 5.34][5.34 .. 5.36]
[5.36 .. 5.38][5.38 .. 5.40]
[5.40 .. 5.42]
[5.42 .. 5.44]
[5.44 .. 5.46][5.46 .. 5.48][6.10 .. 6.12][6.12 .. 6.14][6.14 .. 6.16][6.16 .. 6.18][6.18 .. 6.20][6.20 .. 6.22][6.22 .. 6.24]
[6.24 .. 6.26]
[6.26 .. 6.28][6.28 .. 6.30]
[6.30 .. 6.32][6.32 .. 6.34][6.34 .. 6.36][6.36 .. 6.38][6.38 .. 6.40][6.40 .. 6.42][6.42 .. 6.44][6.44 .. 6.46][6.46 .. 6.48][6.48 .. 6.50][6.50 .. 6.52]
[6.52 .. 6.54]
[6.54 .. 6.56][6.56 .. 6.58][6.58 .. 6.60][6.60 .. 6.62]
[6.62 .. 6.64]
[6.64 .. 6.66][6.66 .. 6.68][6.68 .. 6.70][6.70 .. 6.72][6.72 .. 6.74]
[6.74 .. 6.76][6.76 .. 6.78][6.78 .. 6.80]
[6.80 .. 6.82]
[6.82 .. 6.84]
[6.84 .. 6.86]
[6.86 .. 6.88]
[6.88 .. 6.90][6.90 .. 6.92]
[6.92 .. 6.94][6.94 .. 6.96]
[6.96 .. 6.98][6.98 .. 7.00][7.00 .. 7.02][7.02 .. 7.04]
[7.04 .. 7.06]
[7.06 .. 7.08][7.08 .. 7.10][7.10 .. 7.12][7.12 .. 7.14]
[7.14 .. 7.16][7.16 .. 7.18]
[7.18 .. 7.20][7.20 .. 7.22]
[7.22 .. 7.24][7.24 .. 7.26][7.26 .. 7.28][7.28 .. 7.30]
[7.30 .. 7.32][7.32 .. 7.34]
[7.34 .. 7.36]
[7.36 .. 7.38] [7.38 .. 7.40]
[7.40 .. 7.42][7.42 .. 7.44]
[7.44 .. 7.46]
[7.46 .. 7.48][7.48 .. 7.50][7.50 .. 7.52][7.52 .. 7.54]
[7.54 .. 7.56]
[7.56 .. 7.58]
[7.58 .. 7.60][7.60 .. 7.62]
[7.62 .. 7.64][7.64 .. 7.66]
[7.66 .. 7.68]
[7.68 .. 7.70][7.70 .. 7.72]
[7.72 .. 7.74][7.74 .. 7.76][7.76 .. 7.78][7.78 .. 7.80][7.80 .. 7.82]
[7.82 .. 7.84][7.84 .. 7.86]
[7.86 .. 7.88][7.88 .. 7.90][7.90 .. 7.92]
[7.92 .. 7.94][7.94 .. 7.96][7.96 .. 7.98][7.98 .. 8.00][8.00 .. 8.02]
[8.02 .. 8.04][8.04 .. 8.06]
[8.06 .. 8.08]
[8.08 .. 8.10]
[8.10 .. 8.12][8.12 .. 8.14][8.14 .. 8.16][8.16 .. 8.18]
[8.18 .. 8.20][8.20 .. 8.22]
[8.22 .. 8.24][8.24 .. 8.26][8.26 .. 8.28][8.28 .. 8.30][8.30 .. 8.32]
[8.32 .. 8.34][8.34 .. 8.36][8.36 .. 8.38][8.38 .. 8.40][8.40 .. 8.42]
[8.42 .. 8.44][8.44 .. 8.46]
[8.46 .. 8.48]
[8.48 .. 8.50][8.50 .. 8.52]
[8.52 .. 8.54][8.54 .. 8.56][8.56 .. 8.58][8.58 .. 8.60][8.60 .. 8.62][8.62 .. 8.64][8.64 .. 8.66][8.66 .. 8.68][8.68 .. 8.70][8.70 .. 8.72][8.72 .. 8.74][8.74 .. 8.76][8.76 .. 8.78][8.78 .. 8.80]
[8.80 .. 8.82][8.82 .. 8.84]
[8.84 .. 8.86]
[8.86 .. 8.88][8.88 .. 8.90]
[8.90 .. 8.92]
[8.92 .. 8.94][8.94 .. 8.96]
[8.96 .. 8.98]
[8.98 .. 9.00][9.00 .. 9.02][9.02 .. 9.04][9.04 .. 9.06][9.06 .. 9.08][9.08 .. 9.10]
[9.10 .. 9.12]
[9.12 .. 9.14]
[9.14 .. 9.16][9.16 .. 9.18][9.18 .. 9.20][9.20 .. 9.22][9.22 .. 9.24]
[9.24 .. 9.26]
[9.26 .. 9.28]
[9.28 .. 9.30]
[9.30 .. 9.32][9.32 .. 9.34][9.34 .. 9.36][9.36 .. 9.38][9.38 .. 9.40][9.40 .. 9.42][9.42 .. 9.44][9.44 .. 9.46][9.46 .. 9.48][9.48 .. 9.50][9.50 .. 9.52][9.52 .. 9.54][9.54 .. 9.56][9.56 .. 9.58][9.58 .. 9.60][9.60 .. 9.62][9.62 .. 9.64][9.64 .. 9.66][9.66 .. 9.68][9.68 .. 9.70][9.70 .. 9.72][9.72 .. 9.74][9.74 .. 9.76][9.76 .. 9.78][9.78 .. 9.80][9.80 .. 9.82][9.82 .. 9.84][9.84 .. 9.86][9.86 .. 9.88][9.88 .. 9.90][9.90 .. 9.92][9.92 .. 9.94][9.94 .. 9.96][9.96 .. 9.98]
SIMCA-P+ 10.5 - 02/09/2004 11:09:27
Citrate
2-oxoglutarateand succinate
A major benefit of Intelligent Bucketing can alsobe seen in Figure 5. Here the predictive quality(Q2) of the PCA components is shown for each ofthe bucketing methods. Most notably, the Q2
value is negative for Principal Components 2, 3,and 5, with Classical Bucketing indicting thatanything other than a 1 component model wouldhave no predictive value. In the PCA modelsproduced with the intelligent buckets, the Q2 arepositive, indicting some predictive quality. Thegenerally low values for Q2 are due to the lownumber of samples in the data set.
Figure 5 - In these plots, the quality of the PCAmodels is shown. This Q2 value for the data fromthe intelligent buckets, is much improved overthat from the classical buckets.
CONCLUSION
Intelligent Bucketing has been shown to allow forimprovements to the accuracy of the modeling ofNMR data by removing an inherent failing ofClassical Bucketing. This failing arises from theinherent sensitivity of NMR to the molecularenvironment; most notably, the sensitivity to pHsubtleties, changes in temperature, and thepresence of ion interactions.
REFERENCES
1. Intelligent Bucketing for Metabonomics—Part 1,http://www.acdlabs.com/download/publ/2004/enc04/intelbucket.pdf
2. Nicholls A.W., Mortishire-Smith, R.J. andNicholson, J.K. (2003) NMR spectroscopic andmetabonomic studies of urinary variation inacclimatising germ free rats. Chemical Researchin Toxicology, 16(11):1395-1404.
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