Download Pattern mining of mass spectrometry quality control data

9th Benelux Bioinformatics Conference, 09/12/2014
Pattern mining of mass spectrometry
quality control data
Wout Bittremieux
Mass spectrometry
protein
digestion
protein sample
peptide
separation
peptide sample
output
ion selector
ion source
fragment
mass analyzer
fragmentation
generalized mass spectrometer
3
detector
spectra
Quality control metrics
Derived from experimental
data
Instrument settings
Walzer, M. et al. qcML: An exchange format for quality control metrics from mass spectrometry experiments. Molecular & Cellular Proteomics 13, 1905–1913 (2014).
Bittremieux, W. et al. jqcML: An open-source Java API for mass spectrometry quality control data in the qcML format. Journal of Proteome Research 13, 3484–3487 (2014).
Bittremieux, W. et al. Mass spectrometry quality control through instrument monitoring. In preparation.
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Metrics derived from experimental data
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Metrics derived from experimental data
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Metrics derived from experimental data
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Metrics derived from experimental data
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Metrics derived from experimental data
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Metrics derived from experimental data
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Instrument settings
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Instrument settings
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Instrument settings
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Instrument settings
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Instrument settings
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Instrument settings
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High dimensionality
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Previous approaches: Univariate
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Previous approaches: Multivariate
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Previous approaches: Multivariate
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Our approach: Subspace clustering
•
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Try to find a suitable subset of the original feature space
in which (dis)similar items can be found
Experiment
QC1
QC2
QC3
QC4
Exp1
5
6
1000
2000
Exp2
6
8
170
150
Exp3
7
6
140
160
Exp4
3000
400
160
110
Our approach: Subspace clustering
•
22
Try to find a suitable subset of the original feature space
in which (dis)similar items can be found
Experiment
QC1
QC2
QC3
QC4
Exp1
5
6
1000
2000
Exp2
6
8
170
150
Exp3
7
6
140
160
Exp4
3000
400
160
110
Our approach: Subspace clustering
•
23
Try to find a suitable subset of the original feature space
in which (dis)similar items can be found
Experiment
QC1
QC2
QC3
QC4
Exp1
5
✓
8
1000
2000
Exp2
✓
6
6
170
150
Exp3
7
6
140
160
Exp4
3000
400
160
110
Our approach: Subspace clustering
•
24
Try to find a suitable subset of the original feature space
in which (dis)similar items can be found
Experiment
QC1
QC2
QC3
QC4
Exp1
5
✓
8
1000
✗
170
2000
Exp2
✓
6
6
Exp3
7
6
140
160
Exp4
3000
400
160
110
✗
150
Our approach: Subspace clustering
•
25
Try to find a suitable subset of the original feature space
in which (dis)similar items can be found
Experiment
QC1
QC2
QC3
QC4
Exp1
5
6
1000
2000
Exp2
6
8
170
150
Exp3
7
6
140
160
Exp4
3000
400
160
110
Frequent itemset mining
Aksehirli, E. et al. Cartification: A neighborhood preserving transformation for mining high dimensional data. in 13th IEEE International Conference on Data Mining 937–942 (2013).
Naulaerts, S. et al. A primer to frequent itemset mining for bioinformatics. Briefings in Bioinformatics (2013).
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Cartification
Transactions consist of the k nearest neighbors on a single
dimension for each item
10
11
20
9
7
8
16
6
12
4
8
5
3
4
1
2
27
0
4
8
12
16
20
Cartification
Transactions consist of the k nearest neighbors on a single
dimension for each item
10
1
11
20
9
7
8
16
6
12
4
8
5
3
4
1
2
28
0
4
8
12
16
20
2
3
Cartification
Transactions consist of the k nearest neighbors on a single
dimension for each item
10
1
2
3
1
2
3
11
20
9
7
8
16
6
12
4
8
5
3
4
1
2
29
0
4
8
12
16
20
Cartification
Transactions consist of the k nearest neighbors on a single
dimension for each item
10
1
2
3
1
2
3
2
3
4
11
20
9
7
8
16
6
12
4
8
5
3
4
1
2
30
0
4
8
12
16
20
Cartification
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k-nearest neighbors in the first
k-nearest neighbors in the
dimension (X-axis)
second dimension (Y-axis)
1 2 3
1 2 3
2 3 4
1 2 3
1 2 3
1 3 5
3 4 5
3 4 5
5 6 7
3 4 5
3 4 5
4 6 8
7 8 9
7 8 9
8 9 10
7 8 9
7 8 9
7 9 11
9 10 11
9 10 11
9 10 11
9 10 11
Cartification
Frequent itemset mining: 4 maximal frequent itemsets
with support = 4
10
11
20
9
7
8
16
6
12
4
8
5
3
4
1
2
32
0
4
8
12
16
20
CartiClus
1. Convert the high-dimensional database to a transaction
database
2. Mine (maximal) frequent itemsets
3. Convert the itemsets to subspace clusters
4. Redo clustering projected on the detected subspaces
(optional)
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CartiClus
1.0
F1 score
0.8
0.6
0.4
0.2
0.0
CartiClus
FIRES
1
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PROCLUS
2
4
8
16
32
100
STATPC
200
SUBCLU
Results
Detected subspaces
•
•
•
Various quartiles of the same metric
Related metrics: significant overlap with previous manually
defined groups of co-occurring metrics
New relationships between metrics to be validated using
expert knowledge
Detected clusters
• Highly dependent on projected subspaces
• Able to capture valid relationships between experiments
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Results
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Results
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Conclusion
Different sources of qualitative data
• Metrics derived from experimental data
• Instrument settings
Subspace clustering to detect patterns in high-dimensional
data
• Univariate insufficient: metrics influence each other
• Multivariate insufficient: global transformation
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Conclusion
Cartification: Neighborhood-preserving transformation
• Finds relevant subspaces and discards noise
• Fast
Resulting subspace clustering
• Able to identify relationships between various
qualitative metrics
• Clusters experiments exhibiting similar behavior
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Acknowledgments
ADReM / biomina
Emin Aksehirli
Bart Cuypers
Aida Mrzic
Stefan Naulaerts
Pieter Meysman
Bart Goethals
Kris Laukens
InSPECtor
Hanny Willems
Lennart Martens
Dirk Valkenborg
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biomina