Statistical Classification for Gene Analysis based on Micro-array Data Fan Li & Yiming Yang In collaboration with Judith Klein-Seetharaman

DNA clones PCR purification Reverse transcription Robot printing Hybridize target to microarray Excitation Laser 1 Laser 2 Emission Computer analysis Label with Fluorescent dyes G. Gibson et al. Reference Treated sample Principles of cDNA microarray

Microarray data : how it looks like ? Expression level of a gene across treatments Expression profiles of genes in a certain condition Exp 1 Exp 2 Exp 3 Exp i Exp M G 1 G 2 G N-1 G N Typical examples Heat shock, G phase in cell cycle, etc …conditions Liver cancer patient, normal person, etc …samples Expression matrix

AML/ALL micro-array dataset This dataset can be downloaded from Maxtrix Each Row – a gene Each column – a patient (a sample) Each patient belong to one of two diseases types: AML(acute myeloid leukemia) or ALL (acute lymph oblastic leukemia) disease The 72 patient samples are further divided into a training set(including 27 ALLs and 11 AMLs) and a test set(including 20 ALLs and 14 AMLs). The whole dataset is over 7129 probes from 6817 human genes.

Published work on AML/ALL Classification task: gene expression -> {AML, ALL} Techniques: Support Vector Machings (SVM), Rocchio- style and logistic regression classifiers Main findings: classifiers can get a better performance when using a small subset (8) of genes, instead of thousands Implication: Many genes are irrelevant or redundant?

Possible Relationship (Hypothesis) disease Gene6 Gene8 Gene5 Gene4Gene3 Gene2 Gene1 Gene7

How can find such a structure? Find the most informative genes (“primary” ones) Statistical feature selection (brief) Find the genes related (or “similar”) to the primary ones Unsupervised clustering (detailed) based on statistical patterns of gene distributed over microarrays Bayes network for causal reasoning(future direction)

Possible Relationship (Hypothesis) disease Gene2 Gene1 Gene6 Gene8 Gene5 Gene4 Gene3 Gene7

Feature selection Choose a small subset of input variable (a few instead of genes, for example) In text categorization Features = words in documents Output variables = subject categories of a document In protein classification Features = amino acid motifs … Output variables = protein categories In genome micro-array data Features = “useful” genes Output variables = diseased or not of a patient

Feature selection on micro-array (ALM vs ALL) Golub-Slonim: GS-ranking (filtering method) Ben-Dor TNoM-ranking (filtering method) Isabelle-Guyon: Recursive SVM(Wrapper method) Selected 8 genes (out of in that dataset) Accuracy 100% Our work (Fan & Yiming) (best) Selected 3 genes (using Ridge regression) Accuracy 100%

Feature selection experiments already done in this micro-array data The 3 genes we found Id1882: CST3 Cystatin C(amyloid angiopathy and cerebral hemorrhage) M27891_at Id6201: INTERLEUKIN-8PRECURSOR Y00787_at Id4211: VIL2 Villin 2(ezrin) X51521_at

Some analysis on the result we get The first two genes are strongly correlated with each other. The third gene is very different from the first two genes. 1 st gene + 2 nd gene is bad (10/34 errors) 1 st gene + 3 rd gene is good (1/34 error)

Question:As the next step, Can we find more gene-gene relationship? Several techniques available: Clustering Bayesian network learning Independent component analysis …

Clustering Analysis in micro-array data Clustering methods have already been widely used to find similar genes or common binding sites from micro-array data. A lot of different clustering algorithms… Hierarchical clustering K-means SOM CAST ……

A example of hierarchical clustering analysis(from Spellman et al.)

Our clustering experiment on AML/ALL dataset Our clustering result is over the top 1000 genes most relevant to the disease.

The feature-selection curve

Our clustering result in the top 1000 genes

Some analysis to the clustering result The first two genes are always clustered in the same cluster (in hierarchical clustering, they are in cluster 1. In k-means clustering, they are in cluster 2) The third gene is always not clustered in the same group with the first two genes (in hierarchical clustering, it is in cluster 23. In k-means clustering, it is in cluster 1) This validates our previous analysis.

Disadvantage of Clustering However… It can not find out the internal relationship inside one cluster It can not find the relationship between clusters genes connected to each other may not be in the same cluster. Clustering vs Bayesian network learning (copied from David K,Gifford, Science, VOL293, Sept,2001)

A counter example of clustering analysis

Bayesian network learning Thus Bayesian network seems a much better technique if we want to model the relationship among genes. Researcher have done experiments and constructed bayesian networks from micro- array data. They found there are a few genes which have a lot of connections with other genes. They use prior biology knowledge to validate their learned edges(interactions between genes and found they are reasonable)

A example of the bayesian network Part of the bayesian network Nir Friedman constructed. There are total 800 genes(nodes) in the graph. These 800 genes are all cell-cycle regulated genes.

Our plan in genetic regulatory network construction There are several possible ways Using feature selection technique to make the network learning task more robust and with less computational cost. Learning gene regulatory networks on microarray dataset with disease labels(thus we may find pathways relevant to specific disease). Using ICA to finding hidden variables(hidden layers) and check its consistency with bayes network learning result.

Our plan in genetic regulatory network construction Use prior prior biology knowledge in gene network,like the “network motifs”. The following example is copied from Shai S.Shen-Orr, Naturtics,genetics, Previous network learning algorithm have not considered those characters.

Reference Using Bayesnetwork to analyze Expression Data, Nir Friedman, M.Linial, I.Nachman, Journal of Computational Biology, 7: , Gene selection for cancer classification using support vector machines. Guyon,I.et al. Machine Learning,46, Clustering analysis and display of genome-wide expression patterns, Eisen,M.B. et al. PNAs, 95: , 1998 Clustering gene expression patterns. Ben-Dor, A.,Shamir,R., and Yakini,Z., Computational Biology, 6(3/4): , 1999.