Microarrays: algorithms for knowledge discovery in oncology and molecular biology

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Microarrays algorithms for knowledge discovery
in oncology and molecular biology

• Frank De Smet
• Katholieke Universiteit Leuven
• Faculteit Toegepaste Wetenschappen
• Departement Elektrotechniek (ESAT)
• Promotor Prof. dr. ir. B. De Moor

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Overview

• Introduction basic concepts of microarray data
• Feature extraction
• Univariate analysis
• Multivariate analysis PCA
• Classification
• Clustering
• Conclusions and future research

Introduction Feature extraction Classification
Clustering Conclusions
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Transcription - Translation
Introduction Feature extraction Classification
Clustering Conclusions
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Microarrays
Introduction Feature extraction Classification
Clustering Conclusions
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Importance

• Clinical (oncology)
• Clinical management of cancer is in many cases
  empirical and not all information that is
  clinically relevant can be extracted using the
  data that physicians have access to
• Fundamental mechanisms behind carcinogenesis are
  not always taken into account
• But
• Expression patterns measured with microarrays in
  malignant cells reflect the phenotype of the
  tumour
• Molecular biology
• Study of the expression behaviour of genes can
  help to determine their biological role or
  function

Introduction Feature extraction Classification
Clustering Conclusions
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Data-mining framework
Introduction Feature extraction Classification
Clustering Conclusions
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Expression matrix
Introduction Feature extraction Classification
Clustering Conclusions

Microarray experiments





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Univariate analysis in microarray data
Introduction Feature extraction Classification
Clustering Conclusions
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Multiple testing
Introduction Feature extraction Classification
Clustering Conclusions

• Overlap of the p-values of the genes with and
  without actual differential expression Type I
  and II errors
• In literature control of the Type I error too
  conservative for microarray data
• Here balance of Type I and II error

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Estimation of Type I and II error
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Calculations
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ROC curve

• Optimal balance between Type I and II errors
• Area under the curve
• Quantifies how well the genes whose expression is
  and is not affected by the difference between
  conditions can be discriminated using their
  p-values
• Quality measure for microarray data

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Example Acute leukemia
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Multivariate analysis in microarray
dataPrincipal Component Analysis
Introduction Feature extraction Classification
Clustering Conclusions
Unsupervised
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Classification
Introduction Feature extraction Classification
Clustering Conclusions
Unsupervised
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Clustering gene expression profiles

• Importance
• Identification of groups of coexpressed genes
• Have a higher probability of having similar
  biological functions e.g., might interact with
  the same transcription factors (coregulation)
• First generation algorithms disadvantages
• Parameter fine-tuning
• Assign each profile to a cluster
• Computational complexity

Introduction Feature extraction Classification
Clustering Conclusions
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Quality-based clustering (Heyer et al.)

• Algorithm produces clusters with
• a quality guarantee (fixed and user-defined
  threshold for diameter D)
• with a maximum number of profiles

Introduction Feature extraction Classification
Clustering Conclusions
D
? Still some disadvantages !
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Adaptive quality-based clustering (AQBC)

• A heuristic iterative two-step approach
• Step 1 Quality-based approach
• Find a cluster center in an area of the data set
  where the density of expression profiles, within
  a sphere with preliminary radius, is locally
  maximal
• Step 2 Adaptive approach
• Re-estimation of the radius

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Step 1 Localization of a cluster center
R
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Step 2 Re-calculation of the radius
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Comparison
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Validation
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Availability
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Conclusions

• Data-mining framework for microarray data
• Feature extraction
• Univariate analysis
• Estimation of n1 and n0
• ROC curves optimal balance between Type I and II
  error quality measure
• Multivariate analysis PCA
• Classification FDA and LS-SVM
• Clustering
• Microarray experiments
• Gene expression profiles AQBC
• Clinical data

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Selected publications

• De Smet, F., Marchal, K., Timmerman, D., Vergote,
  I., De Moor, B. and Moreau, Y. (2001) Gebruik van
  microroosters in de klinische oncologie, Tijdschr
  voor Geneeskunde, 57, 1225-1236.
• De Smet, F., Mathys, J., Marchal, K., Thijs, G.,
  De Moor, B. and Moreau Y. (2002) Adaptive
  quality-based clustering of gene expression
  profiles. Bioinformatics, 18, 735-746.
• Moreau, Y., De Smet, F., Thijs, G., Marchal, K.
  and De Moor, B. (2002) Functional bioinformatics
  of microarray data from expression to
  regulation. Proceedings of the IEEE, 90,
  1722-1743.
• De Smet, F., Moreau, Y., Tmmerman, D., Vergote,
  I. and De Moor, B. (2004) Balancing false
  positives and false negatives for the detection
  of differential expression in malignancies. Br J
  Cancer, submitted.
• Epstein, E., Skoog, L., Isberg, P.E., De Smet,
  F., De Moor, B., Olofsson, P.A., Gudmundsson, S.
  and Valentin, L. (2002) An algorithm including
  results of gray-scale and power Doppler
  ultrasound examination to predict endometrial
  malignancy in women with postmenopausal bleeding.
  Ultrasound Obstet Gynecol, 20, 370-376.

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Future research

• Specific
• Ovarian cancer transcriptomics
• Prediction of chemosensitivity in stage III
• Prediction of recurrence in stage I
• Endometriosis proteomics and transcriptomics
• Detection of endometriosis
• Prediction of relapse after surgery
• General
• Microarrays number of patients - validation -
  standardization
• Proteomics
• Combination and comparison of microarray,
  proteomic and clinical data

Introduction Feature extraction Classification
Clustering Conclusions