Predictive Analysis of

Gene Expression Data from

Human SAGE Libraries

Alexessander Alves* Nikolay Zagoruiko+ Oleg Okun§

Olga Kutnenko+ Irina Borisova+

* University of Porto, PORTUGAL+ Russian Academy of Sciences RUSSIA§ University of Oulu FINLAND

Outline

1. Goals

2. Background

3. SAGE Data

4. Gene Expression Data

5. Feature Selection

6. GRAD

7. Experiments

8. Conclusions

Goal

Predictive Analysis:• Feature Selection Methods in Bioinformatics

and Machine Learning

• Cancer Classification

Background

Genes code proteins and other larger biomolecules

Genes are expressed in a two steps process (Central Dogma of Biology)

Several technologies measure transcription: SAGE, Micro array…

Central Dogma of Biology

Gene Expression Process

1- Transcribed into an RNA Sequence

2- Translated into a protein

Molla et al, 2003

SAGE DATA

Advantages:• Compare samples between different organs

and patients. (No normalisation required)

• Collects complete gene expression profile of a cell/tissue without prior knowledge of the mRNA to be profiled

SAGE DATA

Drawbacks:• Very Expensive to Collect Data using the

SAGE method

• Very Few Examples (consequence)

GENE EXPRESSION DATA

Challenges posed to Machine Learning• Number of Genes Dramatically Exceeds

Examples!!!

• Curse of Dimensionality (not enough density to estimate accuratelly the model)

• Over-fitting (higher probability of finding casual relationships among data attributes)

Remove Irrelevant and Redundant Genes Methods:

• Wrapper• Fit classifier to a subset of data and use classification accuracy to

drive the search for relevant genes (e.g. C4.5 accuracy )

• Filtering• Use a function to assess the goodness of a subset of genes (e.g.

euclidean distance, entropy, correlation, etc...) Problem Complexity

• O(2n) ... • n, number of genes• Smaller dataset n=822. • O(2n) 2.8x10246 Intractable using a simple exaustive search

Feature Selection

Gene Selection In Bioinformatics Filtering is usually prefered because is

computationally less expensive Several works on classification select genes

with:• Wilcoxon test, • t-test • Additionally, also remove genes with low entropy,

variability, or absolute expression level. Cons

• Redundancy• Interdependency unaware

Our Proposals

Study Bioinformatics Filtering Techniques

Compare with Machine Learning Algorithms

• Avoid Redundancy

• Consider Interdependency and low expressed genes

Introduce a new Filtering Algorithm GRAD

GRAD

Search StrategySearch Strategy1. Use Exaustive Search on the formation of

informative groups of attributes (“granules”)

2. Use AdDel for choosing subsets of granules

• AdDel: A combination of forward sequential search (FSS) and backward sequential search (BSS)

• Number of attributes to include on a subset is estimated by algorithm

and are the distances to closest neighbors, one from each class

GRAD

AlgorithmAlgorithmP0: x1,x2,…,xn – initial set of features

Formation of granules: Ordering by individual relevanceG1: x7, x33, x12,…,xn All pairs by exhaustive searchG2: x3x8, x15x88,…,xi xj All triplets by exhaustive searchG3: x75x1x35, x11x49x55,…, xi xj xk Top level most relevant granules using AdDel• G=<G1,G2,G3>… AdDel

),(21 211 rrrf 1r 2r

Experiments Comparison

1. GRAD2. Wrapper C4.53. Original Dataset4. Filtering

– Wilcoxon Test, low entropy, variability, and very low absolute expression level

Classifiers1. C4.52. SVM3. RBF 4. NN-MLP

Data• Small Dataset: 74x822

Data Characterization

Not all organs have samples of both classes

Unbalanced number of cases:

• 50 Cancer Samples

• 24 Normal Samples

Most data is relativelly low expressed

Mean quite far from median:

Potentially due to outliers

Data Characterization

average vs standard deviation average vs range

Both range and standard deviation have roughly linear relationship with gene expression level average

Experimental Results

Predictive AccuracyGRAD WRAPPER Original Filtering

86% 82% 79% 78%

GRAD is significantly better than using the original or the filtered dataset

Wrapper approach is not

GRAD Results

Importance of considering dependence Distance Function:

10 best by GRAD P=100 %

10 most individually informative P=75,7 %

),(21 211 rrrf

GRAD Results

Scatter Plot of GRAD Attributes

Interdependency relationship between two non differentially expressed genes selected with GRAD

Two differentially expressed genes selected with GRAD.

GRAD Results

Examples ordered by the value of the Distance Function

In the future it can allow to estimate the degree of risk, to make early diagnostics and to supervise a course of treatment

Induced Classifiers

C4.5 Induced on GRAD attributes C4.5 Induced using a Wrapper Approach

Conclusions

1. Coping with redundancy and dependency between attributes is very important.

2. Algorithm GRAD represents effective means to select a subset of attributes from very big initial set.

3. The submitted results have only illustrative character.

4. We are open for cooperation with those who have interest on the biological interpretation of results

Questions

…

In increasing n the relevance grows, then growth stops and begins its decrease due to addition less informative, rustling attributes.

The maximum of the curve of quality allows to specify optimum quantity of attributes. Only algorithms of AdDel family has such property.

GRAD

Feature Selection

Wrapper• Considers the classifier while searching best subset

• Accuracy Improves

• May overfit due to small sample sizes and huge dimensionality

• Computationally more expensive

Filtering:• Potentially less accurate

• Faster: Does not requires the induction of a predictor

• Commonly prefered approach in bioinformatics