copyright 2003 limsoon wong data mining of gene expression profiles for the diagnosis and...

Copyright 2003 limsoon wong

Data Mining of Gene Expression Profiles for the Diagnosis and

Understanding of Diseases

Limsoon Wong

Institute for Infocomm Research


Plan

• Some accomplishments and challenges in knowledge discovery from biological and clinical data

• Data mining in microarray analysis– diagnosis of disease state and subtype– derivation of treatment plan– understanding of gene interaction network


Knowledge Discovery from Biological and Clinical Data: MOTIVATION


• Complete genomes are now available

• Knowing the genes is not enough to understand how biology functions

• Proteins, not genes, are responsible for many cellular activities

• Proteins function by interacting with other proteins and biomolecules

GENOME PROTEOME

INTERACTOME

Driving Forces: Genes, Proteins, Interactions, Diagnosis, & Cures


If we figure out how these work, we get these Benefits

To the patient:Better drug, better treatment

To the pharma:Save time, save cost, make more $

To the scientist:Better science


To figure these out,we bet on...

“solution” = Data Mgmt + Knowledge Discovery

Data Mgmt =Integration + Transformation + Cleansing

Knowledge Discovery = Statistics + Algorithms + Databases


Knowledge Discovery from Biological and Clinical Data: ACCOMPLISHMENT


IntegrationTechnology(Kleisli)

Cleansing & Warehousing (FIMM)

MHC-PeptideBinding(PREDICT)

Protein InteractionsExtraction (PIES)

Gene Expression & Medical RecordDatamining (PCL)

Gene FeatureRecognition (Dragon)

VenomInformatics

1994 19981996 2000 2002ISS KRDL LIT/I2R

GeneticXchange

MolecularConnections

Biobase

8 years of bioinformatics R&D in Singapore


Predict Epitopes,Find Vaccine Targets

• Vaccines are often the only solution for viral diseases

• Finding & developing effective vaccine targets is slow and expensive process

• Develop systems to recognize protein peptides that bind MHC molecules• Develop systems to recognize hot spots in viral antigens


Recognize Functional Sites,Help Scientists

• Effective recognition of initiation, control, and termination of biological processes is crucial to speeding up and focusing scientific experiments

• Data mining of bio seqs to find rules for recognizing & understanding functional sites

Dragon’s 10x reduction of TSS recognitionfalse positives


Diagnose Leukaemia, Benefit Children

• Childhood leukaemia is a heterogeneous disease

• Treatment is based on subtype• 3 different tests and 4 different

experts are needed for accurate diagnosis

Curable in USA, fatal in Indonesia

• A single platform diagnosis based on gene expression• Data mining to discover rules that are easy for doctors to understand


Understand Proteins,Fight Diseases

• Understanding function and role of protein needs organised info on interaction pathways

• Such info are often reported in scientific paper but are seldom found in structured databases

• Knowledge extraction system to process free text • extract protein names• extract interactions


Data Mining in Microarray Analysis:MICROARRAY BACKGROUND


What’s a Microarray?

• Contain large number of DNA molecules spotted on glass slides, nylon membranes, or silicon wafers

• Measure expression of thousands of genes simultaneously


Affymetrix GeneChip Array


Making Affymetrix GeneChip

quartz is washed to ensure uniform hydroxylation across its surface and to attach linker molecules

exposed linkers become deprotected and are available for nucleotide coupling


Gene Expression Measurement by GeneChip


A Sample Affymetrix GeneChip File (U95A)


Data Mining in Microarray Analysis: DISEASE SUBSTYPE DIAGNOSIS


Pediatric Acute Lymphoblastic Leukemia

• A heterogeneous disease with more than 12 subtypes, e.g., T-ALL, E2A-PBX1, TEL-AML1, BCR-ABL, MLL, and Hyperdip>50.

• Treatment response is subtype dependent

• 80% continuous remission if subtype is correctly diagnosed and the corresponding treatment plan is applied


Subtype Diagnosis

• Require different tests:– immunophenotyping– cytogenetics– molecular diagnostics

• Require different experts:– hematologist– oncologist– pathologist– cytogeneticist


Difficulties and Implications

• The different tests and experts are not commonly available within a single hospital, especially in less advanced countries

An 80%-curable disease in USA can be a fatal disease in Indonesia!

Is there a single diagnostic platform that does not need multiple human specialists?


A Potential Solution by MicroarraysYeoh et al., Cancer Cell 1:133--143, 2002

Diagnostic ALL BM samples (n=327)

3-3 -2 -1 0 1 2 = std deviation from mean

Ge

ne

s f

or

cla

ss

d

isti

nc

tio

n (

n=

27

1)

TEL-AML1BCR-ABL

Hyperdiploid >50E2A-PBX1

MLL T-ALL Novel

TEL-AML1E2A-PBX1

T-ALLHyperdiploid >50BCR-ABL

MLLNovel


Some Caveats

• Study was performed on Americans

• May not be applicable to Singaporeans, Malaysians, Indonesians, etc.

• Large-scale study on local populations currently in the works


Typical Procedure in Analysing Gene Expression for Diagnosis

• Gene expression data collection

• Gene selection

• Classifier training

• Classifier tuning (optional for some machine learning methods)

• Apply classifier for diagnosis of future cases


Feature Selection Methods

A refresher of feature selection methods


Signal Selection (Basic Idea)

• Choose a signal w/ low intra-class distance

• Choose a signal w/ high inter-class distance


Signal Selection (eg., t-statistics)


Signal Selection (eg., 2)


Signal Selection (eg., CFS)

• Instead of scoring individual signals, how about scoring a group of signals as a whole?

• CFS– Correlation-based Feature Selection– A good group contains signals that are highly

correlated with the class, and yet uncorrelated with each other


Gene Expression Profile Classification

An introduction to gene expression profile classificationby the example on ALL subtype diagnosis


Subtype Classification of ALL

A tree-structureddiagnostic workflow was recommended bythe doctors, as perYeoh et al., Cancer Cell 1:133--143, 2002


Training and Testing Sets


Our procedure for ALL subtype diagnosis

• Gene expression data collection

• Gene selection by entropy

• Classifier training by emerging pattern

• Classifier tuning (optional for some machine learning methods)

• Apply classifier for diagnosis of future cases by PCL


Signal Selection (eg., entropy)


Emerging Patterns (EPs)

• An EP is a set of conditions– usually involving several features– that most members of a class satisfy – but none or few of the other class satisfy

• A jumping EP is an EP that – some members of a class satisfy– but no members of the other class satisfy

• We use only most general jumping EPs


PCL: Prediction by Collective Likelihood


Accuracy (using 20 genes of lowest entropy)

PCL

1:1

0:25

0:21:1

0:04

0:1

1:1

1:614

0:11:1

5

0:12:2

7


Comprehensibility


Gene Expression Profile ClassificationHow about other feature selection and

classification methods?


Some gene selection heuristics

• all-CFS: all features from CFS

• top20-2: 20 features w/ highest 2 stats

• top20-t: 20 features w/ highest t-stats

• top20-mit: 20 features w/ highest MIT stats

• entropy: 20 features w/ lowest entropy

• all-2: all features meeting 5% significance level of 2 stats


Some other classification methods

• k-NN (k=1)– majority votes of the k nearest neighbours

determined by Euclidean distance

• C4.5– widely used decision tree method.

• Naïve Bayes (NB)– probabilistic prediction using Bayes’ rule

• SVM– (linear) discriminant function that maximizes

separation of boundary samples


Accuracy

• Feature selection improves performance• Entropy+PCL has consistent high performance


When 20 genes are selected randomly

Average over 100 experiments

Cf. 7-15 mistakes total with good feature selection


Data Mining in Microarray Analysis: TREATMENT PLAN DERIVATION

A pure speculation!


Can we do more with EPs?

• Detect gene groups that are significantly related to a disease

• Derive coordinated gene expression patterns from these groups

• Derive “treatment plan” based on these patterns


Colon Tumour DatasetAlon et al., PNAS 96:6745--6750, 1999

• We use the colon tumour dataset above to illustrate our ideas– 22 normal samples– 40 colon tumour samples


Detect Gene Groups

• Feature Selection– Use entropy method– 35 genes have cut points

• Generate EPs– 19501 EPs in normals– 2165 EPs in tumours

• EPs with largest support are gene groups significantly co-related to disease


Top 20 EPs


Observation 1

• Some EPs contain large number of genes and still have high freq

• E.g., {2, 3, 6, 7, 13, 17, 33} has freq 90.91% in normal and 0% in cancer samples

Nearly all normal sample’s gene expr. values satisfy all conds. implied by these 7 items


Observation 2

• Freq of singleton EP is not necessarily larger than EP having multiple genes

• E.g., {5} is EP in cancer samples and has freq 32.5%

• E.g., {16, 58, 62} is EP in cancer samples and has freq 75.5%

Groups of genes and their correlation's could be more impt than single genes


Observation 3

• M33680 has lowest entropy of the 35 genes if cutpoint is set at 352

• 18/40 of cancer samples shift expr level of M33680 from its normal range to its abnormal range


Treatment Plan Idea

• Increase/decrease expression level of particular genes in a cancer cell so that– it has the common EPs of normal cells– it has no common EPs of cancer cells


Treatment Plan Example

• From the EP {2,3,6,7,13,17,33}– 91% of normal cells express the 7 genes

(T51560, T49941, M62994, R34701, L02426, U20428, R10707) in the corr. Intervals

– a cancer cell never express all 7 genes in the same way

– if expression level of improperly expressed genes can be adjusted, the cancer cell can have one common EP of normal cells

– a cancer cell can then be iteratively converted into a normal one


Choosing Genes to Adjust


Doing more adjustments...

• Down regulating T49941 leads to 2 more top 10 EPs of normal cells to show up in the adjusted T1

• Down regulating X62153 to below 396 and T72403 to below 296 leads to T1 having 9 top 10 EPs of normal cells

• Ave. no. of EPs in normal cells is 9

• So the adjusted T1 now has impt features of normal cells


Next, eliminate common EPs of cancer cells in T1

• 6 more genes (K03001, T49732, U29171, R76254, D31767,

L40992) are adjusted

• All top 10 EPs of cancer cells now disappear from T1

• Ave. no. of top 10 EPs contained in cancer cells is 6

• The adjusted T1 now holds enough common features of normal cells and no features of cancer cells

T1 is converted to normal cells


“Treatment Plan” Validation

• “Adjustments” were made to the 40 colon tumour samples based on EPs as described

• Classifiers trained on original samples were applied to the adjusted samples

It works!


A Big But...

• Effective means for identifying mechanisms and pathways through which to modulate gene expression of selected genes need to be developed


Data Mining in Microarray Analysis:GENE INTERACTION PREDICTION


Beyond Classification of Gene Expression Profiles

• After identifying the candidate genes by feature selection, do we know which ones are causal genes and which ones are surrogates?

Diagnostic ALL BM samples (n=327)

3-3 -2 -1 0 1 2 = std deviation from mean

Ge

ne

s f

or

cla

ss

d

isti

nc

tio

n (

n=

27

1)

TEL-AML1BCR-ABL

Hyperdiploid >50E2A-PBX1

MLL T-ALL Novel


Gene Regulatory Circuits

• Genes are “connected” in “circuit” or network

• Expression of a gene in a network depends on expression of some other genes in the network

• Can we reconstruct the gene network from gene expression data?


Key Questions

For each gene in the network:

• which genes affect it?

• How they affect it?– Positively?– Negatively?– More complicated ways?


Some Techniques

• Bayesian Networks– Friedman et al., JCB 7:601--620, 2000

• Boolean Networks– Akutsu et al., PSB 2000, pages 293--304

• Differential equations– Chen et al., PSB 1999, pages 29--40

• Classification-based method– Soinov et al., “Towards reconstruction of gene

network from expression data by supervised learning”, Genome Biology 4:R6.1--9, 2003


A Classification-based TechniqueSoinov et al., Genome Biology 4:R6.1-9, Jan 2003

• Given a gene expression matrix X– each row is a gene– each column is a sample

– each element xij is expression of gene i in sample j

• Find the average value ai of each gene i

• Denote sij as state of gene i in sample j,

– sij = up if xij > ai

– sij = down if xij ai


• To see whether the state of gene g is determined by the state of other genes– we see whether sij | i

g can predict sgj

– if can predict with high accuracy, then “yes”

– Any classifier can be used, such as C4.5, PCL, SVM, etc.

• To see how the state of gene g is determined by the state of other genes– apply C4.5 (or PCL or

other “rule-based” classifiers) to predict sgj

from sij | i g – and extract the decision

tree or rules used

A Classification-based TechniqueSoinov et al., Genome Biology 4:R6.1-9, Jan 2003


Advantages of this method

• Can identify genes affecting a target gene

• Don’t need discretization thresholds

• Each data sample is treated as an example

• Explicit rules can be extracted from the classifier (assuming C4.5 or PCL)

• Generalizable to time series


Acknowledgements

Vladimir Bajic

See-Kiong NgVladimir Brusic

Huiqing Liu

Jinyan Li


Data Mining in Microarray Analysis: NOTES


References

• J.Li, L. Wong, “Geography of differences between two classes of data”, Proc. 6th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 325--337, 2002

• J.Li, L. Wong, “Identifying good diagnostic genes or gene groups from gene expression data by using the concept of emerging patterns”, Bioinformatics, 18:725--734, 2002

• J.Li et al., “A comparative study on feature selection and classification methods using a large set of gene expression profiles”, GIW, 13:51--60, 2002


References

• E.-J. Yeoh et al., “Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling”, Cancer Cell, 1:133--143, 2002

• U.Alon et al., “Broad patterns of gene expression revealed by clustering analysis of tumor colon tissues probed by oligonucleotide arrays”, PNAS 96:6745--6750, 1999

• L.A.Soinov et al., “Towards reconstruction of gene networks from expression data by supervised learning”, Genome Biology 4:R6.1--9, 2003.


> Data Mining of Gene Expression Profiles for> the Diagnosis and Understanding of Diseases>> This talk is divided into two parts. In Part I, I will provide a> brief overview of some accomplishments and challenges> in Bioinformatics. In Part II, I will discuss the data mining> in the analysis of microarray gene expression profiles for> (a) diagnosis of disease state or subtype, (b) derivation of> disease treatment plan, and (c) understanding of gene> interaction networks.>

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