my phd thesis presentation slides

Hierarchical information representation

and efficient classification

of gene expression microarray data

PhD candidate:

Mattia Bosio Advisors:

Philippe SalembierAlbert Oliveras Vergés

27/06/2014 Mattia Bosio PhD thesis defense 1

Thesis objective

Develop algorithms for microarray classification

–Predictive performance

–Results stability

–Biological interpretability

Roadmap

327/06/2014 Mattia Bosio PhD thesis defense

1- Microarrays

2- Challenges & Opportunities

3- Contributions

4- How did we get there?

5- Conclusions

Challenges & Opportunities

1- Microarrays

A platform to measure gene expression

• Give a picture of the whole cellular state

• Thousands of parallel measures

• Measure how much each gene is being used

• Can be used to discriminate between populations

Microarrays: what do they measure

Microarrays: how do they look like

45’000 ‘Genes’

Challenges & Opportunities2- CHALLENGES &

OPPORTUNITIES

Challenges

Lack of structure

Sample size vs dimensions

45’000 ‘Genes’

Opportunities

• Established tool for research but no optimum algorithm yet for classification

• Machine learning has already been used

– Good results that can be improved

• Signal processing dealt with similar problems

Contributions

3- CONTRIBUTIONS

Two-step classification framework

Feature set

Enhancement

Feature

Selection

Classifier

Train Data

Validation DataClass Estimations

Metagenes1. Metagenes 2. IFFS

3. Ensemble4. Knowledge

Integration

5. Multiclass

algorithm

4- HOW DID WE GET THERE?

4.1 FEATURE SET ENHANCEMENT

A structure is inferred from the data and new metagenes are created.

Feature set enhancementAddresses Noise and Lack of structure

• A binary tree is inferred

• Each node is a new feature

• New features are called metagenes

• Metagenes reduce noise by clustering similar genes

Feature set enhancementThe iterative process of metagene generation

• Iterative process based on Treelets [1]

• The two most similar features are substituted by a metagene

• Two key elements:– Similarity Metric

– Metagene generation algorithm

[1] A. B. Lee, B. Nadler, L. Wasserman, Treelets - an adaptive multi-scale basis for sparse unordered data, Annals of Applied Statistics 2 (2) (2008) 435 {471}.

4.2 FEATURE SELECTION: IFFS

How to select the right features to discriminate between classes with an iterative, wrapperalgorithm

IFFS:Find the few best features to classify

• “Improved Sequential Floating Forward Selection (IFFS)” [2]:

– Sequential, deterministic wrapper algorithm

• Flexible method : at each iteration decide if Add, Delete or Substitute a feature

• Alternatives are compared by a J(·) score

[2] S. Nakariyakul, D. Casasent, An improvement on floating search algorithms for feature subset selection, Pattern Recognition.

IFFS:Find the few best features to classify

Deterministic sequential wrapper algorithm

• All the decisions determined by a J(·) score

• Usually J(·) is an error rate estimation

– Ties are frequent due to the sample scarcity

[2] S. Nakariyakul, D. Casasent, An improvement on floating search algorithms for feature subset selection, Pattern Recognition.

J(·) score tailored for microarrays

Reliability measure to break ties in J(·)

Three rules to define the score combining error rate and reliability:1. Lexicographic sorting2. Exponential penalization3. Linear combination

J(·) score depends on 2 parameters:1. Error rate2. Reliability

IFFS: Experimental setup

• Datasets from MAQC study phase II [4]

• 7 datasets with hundreds of samples

– 30.000+ models evaluated

– Independent validation sets available

– Common evaluation procedure

[4] L. Shi, et al., The microarray quality control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models., Nature biotechnology 28 (2010) 827-38.

IFFS: experiment objectives

• Evaluate if metagenes are useful

• Benchmark with state of the art

• Comparison following MAQC standard:

Matthews Correlation Coefficient

𝑀𝐶𝐶 =𝑇𝑃 ⋅ 𝑇𝑁 − 𝐹𝑃 ⋅ 𝐹𝑁

(𝑇𝑃 + 𝐹𝑃)(𝑇𝑃 + 𝐹𝑁)(𝑇𝑁 + 𝐹𝑃)(𝑇𝑁 + 𝐹𝑁)

Results: Metagenes are useful

• Introducing metagenes gives better results

The proposed framework improves state

of the art results

Observations

• The proposed framework works with both itskey elements

• Metagenes are useful (contrib #1)

• IFFS adapted to microarrays improves the state of the art (contrib #2)

4.3 FEATURE SELECTION: ENSEMBLE

How to select the right features to discriminate between classes with a novel ensemble learning algorithm

Ensemble learning - voting scheme

• Ensemble combine experts with a voting scheme

• One expert for each available feature– Expert = Trained Classifier output on analyzed data

– 1 Expert = 1 feature

• The feature selection becomes an Expert subset selection problem

Accuracy In Diversity [7]

the original algorithm

• Starts with p experts : One for each feature

• Sequentially removes the expert with worst error rate on a subset S

• In [6], a simpler version is defined: Kun algorithm

[6] L. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms.Wiley, 2004.[7]R. E. Banfield, L. O. Hall, K. W. Bowyer, and W. P. Kegelmeyer, “A new ensemble diversity measure applied to thinning ensembles.” in Multiple Classifier Systems, ser. Lecture Notes in Computer Science, T. Windeatt and F. Roli, Eds., vol. 2709. Springer, 2003, pp. 306–316.

Accuracy In Diversity

the original algorithm

• PCDM (d) = % of experts correctly classifying sample i

• S set formed of samples with 𝑙𝑏 ≤ 𝑑 ≤ 𝑈𝑏• The expert with worst error rate on S is excluded

EXPERTS

PCDM VOTE

AID Kun

𝑙𝑏 = 𝜇 ⋅ 𝑑 +1 − 𝑑

𝑙𝑏 = 10%

𝑈𝑏 = 𝛼 ⋅ 𝑑 + 𝜇(1 − 𝑑) 𝑈𝑏 = 90%

Adaptations to microarrays

• Nonexpert: Exclude experts unable to find 2 classes in the training set

• Metagenes : included as experts

• Tie-break rule: the expert upper in the tree is excluded

Ensemble: experiment objectives

• Comparison between AID and Kun ensemble algorithms.

• Benchmark with state of the art.

• Comparison following MAQC standard:

Matthews Correlation Coefficient

𝑀𝐶𝐶 =𝑇𝑃 ⋅ 𝑇𝑁 − 𝐹𝑃 ⋅ 𝐹𝑁

(𝑇𝑃 + 𝐹𝑃)(𝑇𝑃 + 𝐹𝑁)(𝑇𝑁 + 𝐹𝑃)(𝑇𝑁 + 𝐹𝑁)

Ensemble algorithms improve the state of

the art

• Both algorithms improve state of the art

• The simpler Kun algorithm is the best option

Observations

• Ensemble learning feature selection led to encouraging results.

• The proposed ensemble learning improves the state of the art. (contrib #3)

• Tailoring the algorithm to the data benefits the results.

4.4 KNOWLEDGE INTEGRATION

Introducing prior biologial knowledge to improve the metagene generation phase. The aim is to obtain more robust performance and more biologically interpretable gene selections

Integration of external biological data

when producing metagenes

Feature set

Enhancement

Feature

Selection

Classifier

Train Data

Validation DataClass Estimations

New metagenes

Biological Knowledge (MSigDb...)

Objectives of this section

• Measures to quantify biological similarity

• Develop ways to integrate both sources of info

Numerical correlation & Biological similarity

• Benchmarking :Predictive power | Results stability |Biological interpretability

Distances and merging algorithms

• 4 similarity metrics studied:

Godall | Smirnov | NoisyOR | Anderberg

• 2 criteria to merge numerical and biological info

Average | pdf equalization

Experimental setup

• 7 MAQC datasets

• 50-run Monte Carlo experiments

• Novel scoring system integrating Numericalresults and Biological analysis tools

Comparative scoring systemPredictive performance

𝑑 =𝜇

𝜖+𝜎from MCC values

Rank by decreasing 𝑑

= best

Biological analysis

4 parallel analysis toolsGSEA | Biograph | Genie |Enrichr

4 parallel rankings

Average biological rankings

1 3 6 2

Final score = rank average2

The best algorithm has the smallest final score

Predictive power scoring & ranking shows

G_pdf as the best solution

The smallest Final Score is the best alternative

MCC BIO

alysisPred

ictiveR

pdf_equalization average

Compared with state of the art, G_pdf

confirms to be the best alternative

The smallest final score is the best alternative

MCC BIO

Observations about knowledge

integration

• Improved results in terms of results stabilityand interpretability

• Godall similarity with pdf-equalization schemeis the best way to integrate prior databases

• G-pdf performance confirmed against state of the art alternatives too (contrib #4)

4.5 MULTICLASS CLASSIFICATION

Study of a novel algorithm for multiclass classification applying coding theory on multiple binary classifiers

Multiclass approach combining multiple

binary classifiers

• Common methods like One Against All (OAA) or One Against One (OAO) can be improved.

• Information coding good results[119]

• Propose a novel approach with ECOC ideas

[119] E. Tapia, L. Ornella, P. Bulacio, and L. Angelone. Multiclass classication of microarray data samples with a reduced number of genes. BMC Bioinformatics 2011.

Our proposal: OAA+PAA

• Choice to combine several experts:– OAA = one classifier per class

– PAA = one classifier separating each class-pair

• Expert = bit in a codeword

• Class estimation by distance with reference words

1 0 0 0 1 1 1 0 0 0

0 1 0 0 1 0 0 1 1 0

0 0 1 0 0 1 0 1 0 1

0 0 0 1 0 0 1 0 1 1

M binary classifiersh1 h2 … hM

Experiments on 7 public datasets

• Binary classifiers trained with Treelet + IFFS

• Compared with OAA, OAO and state of the art alternatives[119 ]

• 50 run Monte Carlo run of 4:1 cross validation.

Average accuracy

OAA+PAA is better than OAA, OAO and state of the art alternatives

OAA OAO [119] LDPC [119] OAA OAA+PAA L1

Observations about OAA+PAA

• It consistently outperforms OAA and OAO algorithms

• Obtains better accuracy than state of the art alternatives from [119 ]

• OAA+PAA is a valid multiclass algorithm(contrib#5)

5- CONCLUSIONS

Two-step approach is the main

contribution

• Feature set enhancement

– Addresses lack of structure

– Addresses noise

• Feature selection & classification

– Choose the best variables among thousands available with new algorithms

Validated contributions

• Metagenes are helpful for classification

• Tailored IFFS algorithm improves state of the art

• Ensemble learning algorithm led to interesting results

• Knowledge integration framework improves interpretability and robustness

• OAA+PAA as a valid multiclass algorithm

PublicationsBosio M, Bellot P, Salembier P, Oliveras A. “Gene Expression Data Classification Combining Hierarchical Representation and Efficient Feature Selection”. Journal of Biological Systems. 2012;20:349-375.

Bosio M, Bellot P, Salembier P, Oliveras A. “Feature set enhancement via hierarchical clustering for microarray classification”. IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2011. ; 2011. pp. 226 -229

Bosio M, Bellot P, Salembier P, Oliveras A. “Microarray classification with hierarchical data representation and novel feature selection criteria”. In: IEEE 12th International Conference on BioInformatics and BioEngineering. Larnaca, Cyprus; 2012.

Bosio M, Bellot P, Salembier P, Oliveras A. “Multiclass cancer microarray classification algorithm with Pair-Against-All redundancy”. In: The 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’12). Washington, DC, USA; 2012.

Bosio M, Salembier P, Bellot P, Oliveras A. “Hierarchical clustering combining numerical and biological similarities for gene expression data classification”. 35th Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13). Osaka, Japan 07/2013

M. Bosio, Salembier, P., Oliveras, A., and Bellot, P., “Ensemble feature selection and hierarchical data representation for microarray classification”, in 13th IEEE International Conference on BioInformatics and BioEngineering BIBE, Chania, Crete, 2013.

Future research directions

• Study a better use of the tree structure

• Integrate more information sources

• Deepen knowledge for ensemble learning

• Study applicability for Next Generation Seqanalysis or other ‘omics’ platforms

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