Analysing Microarray Data Using Bayesian Network LearningName: Phirun SonSupervisor: Dr. Lin Liu

ContentsAimsMicroarraysBayesian NetworksClassificationMethodologyResults

Aims and GoalsInvestigate suitability of Bayesian

Networks for analysis of Microarray data

Apply Bayesian learning on Microarray data for classification

Comparison with other classification techniques

MicroarraysArray of microscopic

dots representing gene expression levels

Gene expression is the process of DNA genes being transcribed into RNA

Short sections of genes attached to a surface such as glass or silicon

Treated with dyes to obtain expression level

Challenges of Microarray Data

Very large number of variables, low number of samples

Data is noisy and incomplete

Standardisation of data format◦ MGED – MIAME,

MAGE-ML, MAGE-TAB◦ ArrayExpress, GEO,

CIBEX

Bayesian NetworksRepresents conditional

independencies of random variables

Two components:◦Directed Acyclic Graph (DAG)◦Probability Table

MethodologyCreate a program to test

accuracy of classification◦ Written in MATLAB using Bayes Net Toolbox

(Murphy, 2001), and Structure Learning Package (Leray, 2004)

◦ Uses Naive network structure, K2 structure learning, and pre-determined structure

Test program on synthetic dataTest program using real dataComparison of Bayes Net and

Decision Tree

Synthetic DataData created from well-known

Bayesian Network examples◦Asia network, car network, and alarm

networkSamples generated from each

networkTested with naive, pre-known

structure, and with structure learning

Synthetic Data - Results

Asia NetworkLauritzen and Spiegelhalter, ‘Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems’, 1988, pg 164

Correct

Naive 81.0%

K2 Learning 83.4%

Known Graph 85.0%

50 Samples, 10 Folds, 100 IterationsClass Node: Dyspnoea

Correct

Naive 83.1%

K2 Learning 84.3%

Known Graph 85.1%

100 Samples, 10 Folds, 50 IterationsClass Node: Dyspnoea

Synthetic Data - Results

Correct

Naive 53.5%

K2 Learning 58.3%

Known Graph 62.4%

Car NetworkHeckerman, et al, ‘Troubleshooting under Uncertainty’, 1994 pg 13

50 Samples, 10 Folds, 100 IterationsClass Node: Engine Starts

Correct

Naive 56.5%

K2 Learning 58.7%

Known Graph 61.2%

100 Samples, 10 Folds, 50 IterationsClass Node: Engine Starts

Synthetic Data - Results

ALARM Network37 Nodes, 46 ConnectionsBeinlich et al, ‘The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks’, 1989

Correct

Naive 72.4%

K2 Learning 78.7%

Known Graph 89.6%

50 Samples, 10 Folds, 10 IterationsClass Node: InsufAnesth

Correct

Naive 69.0%

K2 Learning 77.8%

Known Graph 93.6%

50 Samples, 10 Folds, 10 IterationsClass Node: Hypovolemia

Lung Cancer Data SetPublically available data sets:

◦ Harvard: Bhattacharjee et al, ‘Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Subclasses’, 2001 11,657 attributes, 156 instances, Affymetrix

◦ Michigan: Beer et al, ‘Gene-Expression Profiles Predict Survival of Patients with Lung Adenocarcinoma’, 2002 6,357 attributes, 96 instances, Affymetrix

◦ Stanford: Garber et al, ‘Diversity of Gene Expression in Adenocarcinoma of the Lung’, 2001 11,985 attributes, 46 instances, cDNA Contains missing values

Feature SelectionLi (2009) provides a feature-

selected set of 90 attributes◦Using WEKA feature selection◦Also allows comparison with Decision

Tree based classification

Discretised data in 3 forms◦ Undetermined values left unknown◦ Undetermined values put into either category – two

category◦ Undetermined values put into another category – three

categoryWEKA: Ian H. Witten and Eibe Frank, ‘Data Mining: Practical machine learning tools and techniques’, 2005.

Harvard Set Harvard Training on Michigan

Harvard Training on Stanford

MATLAB WEKA DT

2-Cat -> 2-Cat NF 95 (99.0%)

95 (99.0%)

95 (99.0%)

2-Cat -> 2-Cat F 94 (97.9%)

93 (96.9%)

92 (95.8%)

3-Cat -> 3-Cat NF 94 (97.9%)

95 (99.0%)

94 (97.9%)

3-Cat -> 3-Cat F 88 (91.7%)

95 (99.0%)

94 (97.9%)MATLAB WEKA DT

2-Cat -> 2-Cat NF 41 (89.1%)

46 (100%) 43 (93.5%)

2-Cat -> 2-Cat F 41 (89.1%)

45 (97.8%)

36 (78.3%)

3-Cat -> 3-Cat NF 41 (89.1%)

46 (100%) 42 (91.3%)

3-Cat -> 3-Cat F 41 (89.1%)

46 (100%) 42 (91.3%)

Michigan Set Michigan Training on Harvard

Michigan Training on Stanford

MATLAB WEKA DT

2-Cat -> 2-Cat NF 150 (96.2%)

154 (98.7%)

153 (98.1%)

2-Cat -> 2-Cat F 144 (92.3%)

153 (98.1%)

150 (96.2%)

3-Cat -> 3-Cat NF 145 (92.9%)

153 (98.1%)

153 (98.1%)

3-Cat -> 3-Cat F 140 (89.7%)

152 (97.4%)

153 (98.1%)MATLAB WEKA DT

2-Cat -> 2-Cat NF 41 (89.1%)

46 (100%) 41 (89.1%)

2-Cat -> 2-Cat F 41 (89.1%)

46 (100%) 40 (87.0%)

3-Cat -> 3-Cat NF 41 (89.1%)

45 (97.8%)

39 (84.8%)

3-Cat -> 3-Cat F 41 (89.1%)

46 (100%) 39 (84.8%)

Stanford Set Stanford Training on Harvard

Stanford Training on Michigan

MATLAB WEKA DT

2-Cat -> 2-Cat NF 139 (89.1%)

153 (98.1%)

139 (89.1%)

2-Cat -> 2-Cat F 139 (89.1%)

150 (96.2%)

124 (79.5%)

3-Cat -> 3-Cat NF 139 (89.1%)

150 (96.2%)

154 (98.7%)

3-Cat -> 3-Cat F 139 (89.1%)

150 (96.2%)

152 (97.4%)MATLAB WEKA DT

2-Cat -> 2-Cat NF 86 (89.6%)

95 (99.0%)

86 (89.6%)

2-Cat -> 2-Cat F 86 (89.6%)

92 (95.8%)

72 (75.0%)

3-Cat -> 3-Cat NF 86 (89.6%)

95 (99.0%)

94 (97.9%)

3-Cat -> 3-Cat F 86 (89.6%)

95 (99.0%)

91 (94.8%)

Future WorkUse structure learning for

Bayesian ClassifiersIncrease of homogeneous dataOther methods of classification