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14-2-2013
1
Metabolomics: data acquisition,
preprocessing & quality control
Theo Reijmers,
Analytical BioSciences, Leiden University
Barcelona, 14-02-2013
carbohydrates
Amino acids
Coenzymes (vitamines)
Amino acids
hormones
nucleotides
lipids
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The metabolome
• Metabolites � chemical compounds with low molecular weight
• Many chemical classes, with different chemical properties (different from proteomics)
• Large differences in abundancemass < 1500 Da
polaritylog P –6 to 14
con
cen
tra
tio
nd
yn
am
ic r
an
ge
10
9
The metabolome
mass < 1500 Da
polaritylog P –6 to 14
con
cen
tra
tio
nd
yn
am
ic r
an
ge
10
9
targeted
NMR
LC-MS
custom
global screen
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Analytical strategies: 1H NMR
Advantages
• Straightforward sample preparation
• High sample throughput (robotic control)
• Chemical shifts stable (if pH kept constant)
• Quantification without standards
• Highly repeatable and reproducible
• Very valuable for identification of isolated metabolites
Disadvantages
• Limited sensitivity
• Identification in complex mixtures
rather difficult
Analytical strategies: LC-MS and GC-MS
• Chromatography: separation of compounds in
sample
• Mass-spectrometry: detection of ions based
on mass-to-charge ratio (m/z)
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Chromatography
Types of interaction:
A. Surface adsorption
B. Solvent partitioning
C. Ion exchange
A B C
Separation of chemical compounds
based on chemical properties chromatogram
Mass spectrometer
ionisationmass
analyserdetector
mass
analyser
separation of charged particles in the gas phase
separation based on mass-to-charge ratio (m/z)
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LC-MS vs GC-MSLiquid C-MS
Advantages:
•Fast
•Efficient
•Sensitive
•Wide range of compounds
Disadvantages:
•Unstable*
•Sensitivity compound dependent
•Ion suppression gives rubbish data
•Relative quantification (if no authentic
standard is available)
Gas C-MS
Advantages:
• Highly reproducible retention times
• Sensitive detection for all metabolites
• Characteristic mass fingerprint (identification!)
Disadvantages:
• Derivatization is needed to include polar analytes
*About as stable as a chocolate teapot in a heatwave. (Wilson 2009)
Demonstration & Competence Lab• Applying technology developed in core in associate projects
with industry, academia, clinics, knowledge institutes
• Validation and implementation of metabolomics platforms
• QA/QC system/error model per metabolite
• Clinical & preclinical studies (projects with partners)
• >15 000 samples/year
• > 2000 metabolites
• Identification pipeline
• Training & hands-on-workshops
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Platforms• Lipid analysis by LC-MS (ca. 300 individual compounds)
• Amine analysis by LC-MS/MS (ca. 120 compounds)
• Oxylipin analysis (ca. 140 compounds)
• Global profiling by RP-LC-MS (ca. 450 compounds identified)
• Global profiling by GC-MS (ca. 150 compounds)
• Global profiling by CE-MS (ca. 300 compounds)
• And more under development
Large Metabolomics Measurement
series DCL
• IOP biomarkers for healthy aging– ±2500 samples, 28 batches
– Measurement time ±28 weeks• Matching project LUMC and NCHA Netherlands centre for healthy Aging
• Dutch Twin Register (NTR) – ±3000 samples, 31 batches
– Measurement time ± 30 weeks• Dutch Twin Register (Nederlands Tweeling Register, NTR)
• DiOGenes Diet, Obesity and Genes– ± 2000 samples, 27 batches
– Measurement time ±14 weeks• NMC Associate project N & H cluster
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Measurement Design
• Randomization, replication & blocking of measurements
• Inclusion of compounds & samples to monitor (& eventually correct for) quality – Internal Standards
– Calibration samples
– Quality Control (QC) samples
– Replicate samples (technical & analytical)
– Blanks
– System suitability samples
– Transfer samples
Typical sample sequence listOrde r Nam e Id Leve l Batch Prepar ation Injection isSample isSST isQC isdQC isBlank isCal isOut lier isSuspe ct Comment
1 Blank Blank 0 5 1 1
2 Blank Blank 0 5 1 1
3 Blank Blank 0 5 1 1
4 Blank Blank 0 5 1 1
5 dSST.C2 dSST.C2 2 5 1 1
6 SST.C2 SST.C2 2 5 1 1
7 dQC dQC 4 5 1 1
8 QC QC 4 5 1 1
9 P5.C6.a C6 6 5 1 1
10 P5.C7.a C7 7 5 1 1
11 P5.C0.a C0 0 5 1 1
12 P5.C1.a C1 1 5 1 1
13 P5.C4.a C4 4 5 1 1
14 P5.C5.a C5 5 5 1 1
15 P5.C2.a C2 2 5 1 1
16 P5.C3.a C3 3 5 1 1
17 P5.C1 0543_090.3.01.0 4 5 1 1
18 P5.D1 0546_094.3.01.0 4 5 1 1
19 P5.E1 0550_076.3.01.0 4 5 1 1
20 QC QC 4 5 1 1
21 Blank Blank 0 5 1 1
22 dQC QC 4 5 1 1 1
23 P5.F1 0553_015.3.15.0 4 5 1 1
24 P5.G1 0555_097.3.01.0 4 5 1 1
25 P5.H1 0556_097.3.01.1 4 5 1 1 1 There might be somethi ng wrong here
26 P5.A2 0559_077.3.05.0 4 5 1 1
27 P5.B2 0561_103.3.01.1 4 5 1 1 1 Something wrong here
28 P5.C2 0563_103.3.01.0 4 5 1 1
29 P5.D2 0564_093.3.03.0 4 5 1 1
30 P5.E2 0570_095.3.01.0 4 5 1 1
31 P5. bE1 0550_076.3.01.0 4 5 2 1
32 P5. bA7 0631_057.3.09.0 4 5 2 1
33 QC QC 4 5 1 1
34 Blank Blank 0 5 1 1
35 dQC dQC 4 5 1 1
36 P5.F2 0571_105.3.04.0 4 5 1 1
37 P5.G2 0573_105.3.03.0 4 5 1 1
38 P5.H2 0574_099.3.02.0 4 5 1 1
39 P5.A3 0575_099.3.01.0 4 5 1 1
40 P5.B3 0577_099.3.03.0 4 5 1 1
41 P5.C3 0578_099.3.01.1 4 5 1 1
42 P5.D3 0581_096.3.01.0 4 5 1 1
43 P5.E3 0582_101.3.01.0 4 5 1 1
44 P5.F3 0584_123.3.01.0 4 5 1 1
45 P5.G3 0585_085.3.01.0 4 5 1 1
46 QC QC 4 5 1 1
47 Blank Blank 0 5 1 1
48 dQC dQC 4 5 1 1
49 P5.H3 0587_085.3.01.1 4 5 1 1
50 P5.A4 0589_095.3.01.1 4 5 1 1
51 P5.B4 0590_105.3.01.0 4 5 1 1
52 P5.C4 0591_105.3.02.0 4 5 1 1
53 P5.D4 0593_077.3.12.1 4 5 1 1
54 P5.E4 0594_077.3.12.0 4 5 1 1
55 P5. bF9 0664_130.3.20.1 4 5 2 1
56 P5. bF10 0678_118.3.01.0 4 5 2 1
57 P5.F4 0597_117.3.02.1 4 5 1 1
58 P5.G4 0598_117.3.02.0 4 5 1 1
59 QC QC 4 5 1 1
60 Blank Blank 0 5 1 1
61 dQC dQC 4 5 1 1
62 P5.H4 0599_117.3.01.1 4 5 1 1
63 P5.A5 0600_117.3.01.0 4 5 1 1
64 P5.B5 0603_098.3.04.0 4 5 1 1
65 P5.C5 0604_098.3.02.0 4 5 1 1
66 P5.D5 0605_098.3.01.0 4 5 1 1
67 P5.E5 0606_098.3.01.1 4 5 1 1
68 P5. bB3 0577_099.3.03.0 4 5 2 1
69 P5. bH3 0587_085.3.01.1 4 5 2 1
70 P5.F5 0607_015.3.16.0 4 5 1 1
71 P5.G5 0608_078.3.02.0 4 5 1 1
72 QC QC 4 5 1 1
73 Blank Blank 0 5 1 1
74 dQC dQC 4 5 1 1
75 P5.H5 0609_078.3.03.0 4 5 1 1
76 P5.A6 0611_078.3.01.0 4 5 1 1
77 P5.B6 0612_088.3.02.0 4 5 1 1
78 P5.C6 0613_088.3.01.0 4 5 1 1
79 P5.D6 0616_085.3.02.0 4 5 1 1
80 P5.E6 0618_094.3.05.0 4 5 1 1
81 P5. bE6 0618_094.3.05.0 4 5 2 1
82 P5. bB10 0673_107.3.05.0 4 5 2 1
83 P5. bG1 0555_097.3.01.0 4 5 2 1
84 P5. bC4 0591_105.3.02.0 4 5 2 1
85 QC QC 4 5 1 1
86 Blank Blank 0 5 1 1
87 dQC dQC 4 5 1 1
88 P5.C3.b C3 3 5 1 1
89 P5.C7.b C7 7 5 1 1
90 P5.C2.b C2 2 5 1 1
91 P5.C6.b C6 6 5 1 1
92 P5.C5.b C5 5 5 1 1
93 P5.C4.b C4 4 5 1 1
94 P5.C0.b C0 0 5 1 1
95 P5.C1.b C1 1 5 1 1
96 P5.F6 0620_107.3.01.0 4 5 1 1
97 P5.G6 0629_092.3.01.1 4 5 1 1
98 P5.H6 0630_092.3.01.0 4 5 1 1
99 QC QC 4 5 1 1
100 Blank Blank 0 5 1 1
101 dQC dQC 4 5 1 1
102 P5.A7 0631_057.3.09.0 4 5 1 1
103 P5.B7 0632_057.3.09.1 4 5 1 1
104 P5.C7 0634_091.3.01.0 4 5 1 1
105 P5.D7 0635_015.3.17.0 4 5 1 1
106 P5.E7 0638_072.3.01.0 4 5 1 1
107 P5.F7 0639_066.3.03.0 4 5 1 1
108 P5.G7 0640_066.3.03.1 4 5 1 1
109 P5.H7 0642_109.3.02.0 4 5 1 1
110 P5.A8 0643_109.3.01.0 4 5 1 1
111 P5.B8 0646_110.3.06.1 4 5 1 1
112 QC QC 4 5 1 1
113 Blank Blank 0 5 1 1
114 dQC dQC 4 5 1 1
115 P5.C8 0647_110.3.01.0 4 5 1 1
116 P5.D8 0648_110.3.03.1 4 5 1 1
117 P5.E8 0649_110.3.03.0 4 5 1 1
118 P5.F8 0650_110.3.06.0 4 5 1 1
119 P5. bH6 0630_092.3.01.0 4 5 2 1
120 P5. bF11 0689_065.3.22.0 4 5 2 1
121 P5.G8 0651_110.3.02.0 4 5 1 1
122 P5.H8 0655_108.3.01.1 4 5 1 1
123 P5.A9 0656_108.3.01.0 4 5 1 1
124 P5.B9 0658_111.3.01.0 4 5 1 1
125 QC QC 4 5 1 1
126 Blank Blank 0 5 1 1
127 dQC dQC 4 5 1 1
128 P5.C9 0659_111.3.02.0 4 5 1 1
129 P5.D9 0661_128.3.01.0 4 5 1 1
130 P5. bF4 0597_117.3.02.1 4 5 2 1
131 P5. bC10 0675_129.3.01.1 4 5 2 1
132 P5.E9 0663_130.3.20.0 4 5 1 1
133 P5.F9 0664_130.3.20.1 4 5 1 1
134 P5.G9 0665_130.3.19.1 4 5 1 1
135 P5.H9 0666_130.3.19.0 4 5 1 1
136 P5.A10 0668_097.3.10.0 4 5 1 1
137 P5.B10 0673_107.3.05.0 4 5 1 1
138 QC QC 4 5 1 1
139 Blank Blank 0 5 1 1
140 dQC dQC 4 5 1 1
141 P5. bB5 0603_098.3.04.0 4 5 2 1
142 P5.C10 0675_129.3.01.1 4 5 1 1
143 P5.D10 0676_129.3.01.0 4 5 1 1
144 P5.E10 0677_118.3.01.1 4 5 1 1
145 P5.F10 0678_118.3.01.0 4 5 1 1
146 P5.G10 0681_118.3.02.0 4 5 1 1
147 P5. bH10 0683_078.3.05.0 4 5 2 1
148 P5. bD4 0593_077.3.12.1 4 5 2 1 1 Only Integrated for TGs
149 P5.H10 0683_078.3.05.0 4 5 1 1
150 P5.A11 0684_065.3.27.0 4 5 1 1
151 QC QC 4 5 1 1
152 Blank Blank 0 5 1 1
153 dQC dQC 4 5 1 1
154 P5.B11 0685_065.3.28.0 4 5 1 1
155 P5.C11 0686_065.3.29.0 4 5 1 1
156 P5.D11 0687_065.3.26.0 4 5 1 1
157 P5.E11 0688_065.3.30.0 4 5 1 1
158 P5.F11 0689_065.3.22.0 4 5 1 1
159 P5.G11 0690_065.3.20.0 4 5 1 1
160 P5.H11 0691_065.3.24.0 4 5 1 1
161 P5.A12 0693_065.3.23.0 4 5 1 1
162 P5.B12 0694_065.3.25.0 4 5 1 1
163 P5.C12 0696_112.3.04.0 4 5 1 1
164 QC QC 4 5 1 1
165 Blank Blank 0 5 1 1
166 dQC dQC 4 5 1 1
167 P5.D12 0697_112.3.04.1 4 5 1 1
168 P5.E12 0699_072.3.02.1 4 5 1 1
169 P5.F12 0692_065.3.21.0 4 5 1 1
170 P5.C0.c C0 0 5 1 1
171 P5.C2.c C2 2 5 1 1
172 P5.C4.c C4 4 5 1 1
173 P5.C6.c C6 6 5 1 1
174 P5.C5.c C5 5 5 1 1
175 P5.C3.c C3 3 5 1 1
176 P5.C7.c C7 7 5 1 1
177 P5.C1.c C1 1 5 1 1
178 P5. bH7 0642_109.3.02.0 4 5 2 1
179 QC QC 4 5 1 1
180 Blank Blank 4 5 1 1
181 Blank Blank 0 5 1 1
182 Blank Blank 0 5 1 1
QC-blank-(dummy) QC sequence at regular intervals
Calibration blocks at regular intervals
Running samples
Technical samples: system cleaning, testing and equilibrating.
Possible outliers are flagged and if confirmed ignored
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Data Acquisition, LC-MS & GC-MS
For one chemical compound, the pattern is approximately the multiplication of a component specific mass profile
and the abundance at a certain retention time
Component specific mass profile:
LC-MS: natural isotopes + adducts (soft ionization)
GC-MS: fragments (hard ionization)
1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
6
Retention time
Inte
ns
ity
M/Z
Inte
nsity
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Raw Data, LC-MS
• Huge amount of data
~1000s mass spectra (retention time scans)
~10.000s ion chromatograms
~1.000.000s (m/z – retention time) pairs
For each sample!
• Complex data
- Noise (detector noise and chemical noise), spikes, background
- Concentration differences between the compounds are rather large and therefore also intensity differences
0 200 400 600 800 1000 1200 14000
2000
4000
6000
8000
10000
12000
14000
16000
18000
scan#
# m
ass
ch
an
ne
ls
number of mass channels selected for processing vs scan number
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Preprocessing, LC-MS• Targeted platforms: vendor preprocessing software
– Expert knowledge => optimized settings
• Untargeted platforms: in-house developed preprocessing software– Conversion of manufacturer formats to common formats (e.g. ‘netcdf’ & ‘mzxml’)
– Centroiding and binning
– Baseline correction
– Alignment
– Peak extraction (asks for an estimate of noise level)
– Matching of peaks over samples
• Result: feature/peak/compound list– m/z & rt: peak area
Centroiding
RAW CENTROIDED
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m/z shifts within a sample
Small m/z shifts probably due to centroid sampling mode MS
spectra and mass fluctuations during recording
Binning
• Binning algorithm: sum intensities within predefined bins = mass ranges
• Definition of bins is a challenge, mostly related to the mass resolution (e.g. resolution = 10 000 �define bin 100.00 – 100.01)
• When done incorrect � large influence on peak extraction steps
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Background correction
TIC
Background corrected
Retention time alignment
0 1000 2000 3000 4000 5000 6000 7000-0.5
0
0.5
1
1.5
2
2.5
3x 10
5
2000 2200 2400 2600 2800 3000 3200-0.5
0
0.5
1
1.5
2
2.5
x 105
detail
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Alignment algorithms
• Dynamic Time Warping (DTW)
– Time point by time point mapping
(dynamic programming)
• Correlation Optimized Warping (COW)
– Piecewise linear, segments instead of
individual time points (dyn. progr.)
• (Semi)-Parametric Warping (PTW, Eilers)
– Global, nonlinear (parametric transfer
function estimation)
target dataset
dataset to align
-optimization of correlation between
the two pieces of each dataset
-not allow large retention time
variation (determined by the slack
parameter t)
Alignment algorithms
• Dynamic Time Warping (DTW)
– Time point by time point mapping
(dynamic programming)
• Correlation Optimized Warping (COW)
– Piecewise linear, segments instead of
individual time points (dyn. progr.)
• Parametric Warping (Eilers)
– Global, nonlinear (parametric transfer
function estimation)
3200 3300 3400 3500-50
0
50
100
150
200
3200 3300 3400 3500-50
0
50
100
150
200
3250 3300 3350 3400 3450 3500 3550
0
20
40
60
80
100
120
140
160
180
200
Warped, detail
3200 3250 3300 3350 3400 3450 3500-50
0
50
100
150
200
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Peak/Feature extraction and peak integration
• XCMS http://metlin.scripps.edu/xcms/index.php
• MetAlign http://www.wageningenur.nl/en/show/MetAlign-1.htm
• TNO-DECO Jellema, et al, Chemom. Intel. Lab. Systems, 104 (10) 132
• MZExtract van der Kloet et al, submitted
TNO-DECOWorks with GC-MS and not too complex LC-MS
Decomposes experimental data into the product of
pure mass spectra and concentration profiles of all
compounds in the sample
Advantages:
-Result is combined mass spectrum (identification!!)
-All samples analyzed at once
Problems / issues:
-Least squares (abundant compounds have large
influence on result)
-Noise level estimation
-Correct binning essential
Jellema, Chemo. Intel. Lab. Systems (2010) 104 132-139.
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Deconvolution
baseline corrected data
reconstructed signalExtracted chromatographic profiles
Extracted mass spectra
0 10 20 30 40 50 600
2
4
6
8
10
12
14
16x 10
6
100 200 300 400 500 600 700 800 900 10000
0.5
1
18
4
76
1
rt: 14.769
100 200 300 400 500 600 700 800 900 10000
0.5
1
18
4
75
9
rt: 14.3868
100 200 300 400 500 600 700 800 900 10000
0.5
1
18
4
62
8
70
47
26
75
7
rt: 13.9818
100 200 300 400 500 600 700 800 900 10000
0.5
1
18
4
78
5rt: 14.5777
0 10 20 30 40 50 600
2
4
6
8
10
12
14x 10
6
0 10 20 30 40 50 600
2
4
6
8
10
12
14x 10
6
Deconvolution of LC-MS data
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MZExtract
Per sample:
•Feature extraction of recalibrated and
centroided data (in-house)
•Integration of features (areas)
•Grouping of features to feature-sets
(enrichment step � knowledge based:
isotopes, adducts)
Over samples:
•Match feature-sets
Advantage of two-step approach: fully scalable
solution (parallel implementation)
van der Kloet, submitted.
Grouping related features within a single sample
No retention time window necessary to
match features (only isotopic patterns or
other known relations, e.g. adducts)
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Validation
Target list from MassHunter (Agilent) used to
locate 174 known targets.
– Mass window -> resolution 10.000
– RT window -> +/- 10 seconds
– 171 were found
– 3 missing targets: no isotopic patterns were
detected (they were found in the list of ‘single’
features)
about 3.200 unknown
feature-sets
How to validate unknown feature-sets?
here: selection based on QC presence
Comparable: 1.175 feature-sets
Low abundant: 366 feature-sets
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PLS-DA, Selectivity ratio*, to quantify the
variables discrimanatory ability
The low abundant feature-sets do contain biological relevance!
The most important feature-sets is an unknown!
*Anal. Chem. 2009, 81, 2581–2590
Quality Assessment
• Make use of all additional measured compounds
and samples
– Internal Standards
– Replicates
– Blanks
– Quality Control samples
• Quality Assessment => QC report (in-house)
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19
Part of a measurement run
Measurement Order
Re
sp
on
se
QC sample
Study sample
Replicate study sample
N mean std RSDqc RSD reps p-value diffs
CholE02 58 0.0298 0.0079 26.4% 21.4% 0.000 (2-1,3-1,3-2,4-2,4-3)
CholE04 46 0.0240 0.0124 51.9% 40.6%
CholE05 58 0.0120 0.0024 20.4% 19.1% 0.000 (2-1,3-1,4-1,3-2,4-3)
CholE06 58 0.0085 0.0021 24.7% 19.5% 0.000 (3-1,3-2,4-3)
DG02 58 0.0049 0.0011 23.4% 22.7% 0.000 (2-1,3-1,4-1,3-2,4-2,4-3)
LPC01 58 0.0183 0.0009 4.7% 4.8% 0.000 (4-1,4-2,4-3)
LPC02 58 0.0130 0.0015 11.7% 11.5% 0.000 (2-1,3-1,4-1)
LPC03 58 0.0101 0.0010 9.5% 12.1% 0.360
LPC04 58 0.0436 0.0019 4.4% 5.4% 0.000 (2-1,4-1,3-2,4-3)
LPC05 58 1.8684 0.1259 6.7% 6.8% 0.000 (2-1,3-1,4-1,3-2,4-2,4-3)
LPC07 58 0.0109 0.0007 6.1% 6.4% 0.004 (4-2)
LPC08 58 0.6096 0.0141 2.3% 3.2% 0.000 (2-1,3-1,4-1,3-2,4-2,4-3)
LPC09 58 0.4170 0.0200 4.8% 4.8% 0.000 (3-1,4-1,3-2,4-2,4-3)
LPC10 58 0.6625 0.0976 14.7% 13.8% 0.000 (2-1,3-1,4-1,3-2,4-2,4-3)
LPC11 58 0.0394 0.0446 113.1% 57.6% 0.000 (2-1,3-2,4-2,4-3)
LPC12 58 0.1126 0.0024 2.1% 3.6% 0.000 (2-1,3-1,3-2,4-2,4-3)
LPC13 58 0.0425 0.0049 11.5% 9.8% 0.000 (3-1,4-1,3-2,4-2)
LPC14 58 0.0311 0.0010 3.3% 3.7% 0.000 (2-1,3-1,4-2,4-3)
LPC16 58 0.0064 0.0016 24.9% 28.7% 0.000 (4-1,3-2,4-2,4-3)
LPC17 58 0.0033 0.0010 32.0% 36.4% 0.000 (3-1,4-1,3-2,4-2,4-3)
LPE02 58 0.0303 0.0056 18.6% 19.4% 0.000 (2-1,4-1,3-2,4-2,4-3)
LPE04 43 0.0034 0.0011 33.1% 21.9%
PC01 58 0.0832 0.0105 12.6% 12.5% 0.000 (4-1,4-2,4-3)
PC02 58 0.3333 0.0151 4.5% 4.6% 0.000 (2-1,4-1,4-2,4-3)
PC03 58 0.2238 0.0077 3.4% 3.7% 0.000 (2-1,3-1,4-1,4-2,4-3)
PC04 58 0.1257 0.0040 3.1% 4.8% 0.000 (3-1,4-1,3-2,4-3)
PC05 58 0.0674 0.0248 36.8% 35.9% 0.000 (2-1,3-1,4-1,3-2,4-3)
PC06 58 0.0667 0.0084 12.7% 10.1% 0.000 (2-1,4-1,3-2,4-3)
PC07 58 0.0225 0.0026 11.5% 14.2% 0.000 (2-1,3-1,4-1,4-2,4-3)
QC report overview tableANOVA for batch to batch variation
RSD values for
• QC samples
• Replicate samples
(independent validation)
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Uncorrected Peak areas
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QC samples only
Ratio (unc)Area
RSD
QC
25.8%
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Internal standard
RSDQC=25.8%
Internal Standard Corrected data
RSDQC=20.6%
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23
Intra and Inter batch variation
• Analytical Column ‘aging’
• Analytical Column replacement
• Eluent ‘refills’ and small variations
• Instrument malfunction/breakdown
– Etc…
Intra and Inter batch correction
• Instead of just monitoring QC sample
responses use them to correct variation
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QC correction
Measurement Order
Re
sp
on
se
QC sample
Study sample
Penalized smoother
Van der Kloet et al., Journal of Proteome Research 2009
QC correction
before after
Measurement Order
Re
sp
on
se
Measurement Order
Re
sp
on
se
Van der Kloet et al., Journal of Proteome Research 2009
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25
QC correction
van der Kloet et al., Journal of Proteome Research 2009
QC correction
van der Kloet et al., Journal of Proteome Research 2009
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ISTD/QC corrected data
RSDQC=4.1%
RSDreplicates=10.0%
All samples
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All batches
Correction charts
RSDQC
RSDReplicates
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28
Scores plot based upon 93 lipids
Uncorrected Area
-15 -10 -5 0 5 10 15 20-15
-10
-5
0
5
10
15
20
25
30
35
PC 1 (39.3%)
PC
2 (
14
%)
Scores plot based on 93 components (Peak Area)
batch 1
batch 2
batch 3
batch 4
QC samples
Differences between batches.
Clear trends in QC samples.
-10 -5 0 5 10 15 20 25 30 35-15
-10
-5
0
5
10
15
PC 1 (21.3%)
PC
2 (
14.8
%)
Scores plot based on 93 components (ISTD correction)
batch 1
batch 2
batch 3
batch 4
QC samples
Scores plot based upon 93 lipids ISTD
correctionSmaller differences between
batches.
Spread in QC samples greatly
reduced. However, batch to batch
differences remain present.
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29
-15 -10 -5 0 5 10 15 20 25 30 35-15
-10
-5
0
5
10
15
20
PC 1 (22.9%)
PC
2 (
14.7
%)
Scores plot based on 93 components RSDqc<0.15 and RSDreps<0.15
Scores plot based upon 93 lipids
batch 1
batch 2
batch 3
batch 4
QC samples
Comprehensive view of patient, animal, … :
e.g. combine genomics, proteomics & metabolomics data
�Data integration / fusion:
joining data from different measurement
approaches, same objects
Increase power of statistical analyses:
Combine e.g. metabolomics batch datasets
���� ‘Equating’: (*)
make comparable data from
same measurement approach, different objects
ob
jects
Combining data in systems biology
1 2
1
2ob
jects
variables
variables
*Equating is psychometrical term
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30
Why not just concatenate datasets?
• ‘Omics data typically batch data
• Metabolomics often not quantitative
� datasets not comparable
• Calibration model transfer would be solution but…
…often no full calibration models can be made!*
*Sangster et al, The Analyst 2006 (131): 1075-1078
1
2ob
jects
variables
?�
A proposed approach: QC samples
Correction for structural differences between series
using quality control (QC) samples (pooled samples
or representative samples)*
*van der Greef et al, J Proteome Res 2007 (6): 1540-1559
(picture from reference below)
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Problem with QC sample approach
• Rationale: make medians of QC data equal for all series
• Unwanted side-effect: inflation of variation in rest of data:
MA
D
MAD: median absolute deviation (robust SD)
Series 1
Series 2, uncorrected
Series 2, QC-corrected
Lipid compounds
Inflation of MAD in series 2 relative to series 1
Alternative solution: equating
• Combination of data from
different measurement series
• …in studies with limited number of
internal standards
(typically metabolomics!)
• …or even from different studies
• General: enables maximal flexibility in subsequent data
analysis on combined datasets
1
2ob
jects
variables
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32
Illustration: LC–MS data
• 182 (54 + 128) healthy participants(Netherlands Twin Register)*
• Blood samples (overnight fasting)
• Plasma analyzed with liquid chromatography–MS method forlipids
�Target list for 59 lipids:LPC / PC / SPM / ChE / TG
�Data per lipid corrected for class-specific internal standard
Measured in two series:
year 1 (Y1) N=54
+
year 2 (Y2) N=128
*Draisma et al, OMICS 2008: 17–31
PCA scores before equating
Data mean-centered prior to PCA
Y1
Y2
Y1
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33
Univariate quantile equating
•Quantiles:
values marking boundaries between regular intervals
of the cumulative distribution function (CDF)
•Example: 54 data values and associated CDF
CDF
0.50 quantile (= median)
0.52 quantile
0.48 quantile
1/54
1/54
Univariate quantile equating
Average values of corresponding quantiles
CDF Y2
CDF Y1
Data from: Frisby & Clatworthy, Perception 1975: 173-178
CDF(x) = 0.50x = 1.81
x = 2.64
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Quantile equating
Algorithm:
1. Number of quantiles = min {N1 , N2, …}
2. Average values of corresponding
quantiles by projection onto unit vector ( )
3. Substitute averaged values for original values belonging
to each quantile
Often applied for quantile normalization (*)
of gene arrays, between arrays (objects) over probes (variables)
nn
1,...,
1
*Bolstad et al, Bioinformatics 2003: 185–193
Projection onto
unit vector:
averaging
Projection onto
unit vector
Example univariate quantile equating
Before
After
Y1
Y2
Q-Q plot
Y1
Y2
CD
F Y
2
CDF Y1
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35
PCA scores after equating LC–MS data
Data meancentered prior to PCA
red: Y1black: Y2
Y1
Y2
Before
After
equating
variance:Box’s M statistic
location:Mahalanobis’ D2
Y1–Y2 similarity in PCA score space*
Y2
*Jouan-Rimbaud et al, Chemom Intell Lab Syst 1998: 129-144
Y1
direction:PCA loadings
PC
3
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36
Y1–Y2 similarity in PCA score space
All parameters: 0 = ‘dissimilar’, 1 = ‘similar’
Before
equatingAfter
equating
Jouan-Rimbaud et al, Chemom Intell Lab Syst (1998) 129-144
location
variance
direction
Effects on clustering results
Y2 Y1
Y2
Y1
No equating,
Y1–Y2 datasets combined:
Obvious
between-series effect
Draisma et al, Anal Chem (2010) 82 1039-1046
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37
Effects on clustering results
After quantile equating,
Y1–Y2 datasets
combined:
Y1–Y2 effect removed
Biological information
extractable from
combined dataset
♂ ♀♂
♀
Draisma et al, Anal Chem (2010) 82 1039-1046
Conclusions
• ‘Garbage in = Garbage out’ so try to control data
quality as much as possible
• Proper measurement design allows separation of
unwanted experimental variation from biological
variation (IS, QCs, replicates)
• Preprocessing: trade off between data quality, speed
(automation) and completeness (number of features)
• Road to high quality data is balanced mix of data
acquisition and data processing
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Acknowledgements
• DCL
– Jorne Troost
– Evelyne Steenvoorden
– Shanna Shi
– Faisa Galud
– Rob Vreeken
– Amy Harms
– Raymond Ramakers
– Irina Paliukovich
– Adrie Dane
• LACDR
– Frans van der Kloet
– Katrin Strassbourgh
– Vanessa Gonzalez
– Margriet Hendriks
– Harmen Draisma
– Thomas Hankemeier
