A New Biclustering Algorithm for Analyzing Biological Data Prashant Paymal Advisor: Dr. Hesham Ali

Introduction Microarray technology use to study the expression of many genes at once Large amount of data is produced in the microarray technology Proper analysis of the data is important to get meaningful information from it There is a need for new analysis techniques

Data Analysis From data to knowledge We need to process data by grouping and synthesizing information into a “big picture” based upon characteristics and relationships One of the most used analysis technique is traditional clustering

Traditional Clustering Applied to either rows or columns of the data matrix separately Each gene is defined using all the conditions Each condition is characterized by the activity of all the genes that belong to it Genes Conditions

Motivation The large amount of data provide us great challenges of analysis Clustering algorithms consider all the conditions to group genes and all the genes to group conditions Biologically data may not show similar behavior in all conditions but in a subset of them Traditional clustering algorithms will very likely miss some important information

Biclustering The term “Biclustering” was first used by Cheng and Church in gene expression data analysis [Year 2000] Clusters do not need to include all parameters (genes in Bioinformatics) for all conditions Data Matrix ▫Each gene – One row ▫Each condition – One column ▫Each element – expression level of a gene under specific condition

Biclustering (Cont.) Performs clustering in these two dimensions simultaneously Each gene is selected using only a subset of the conditions Each condition is selected using only a subset of the genes Genes Conditions

Goal of Biclustering To identify subgroups of genes and subgroups of conditions by performing simultaneous clustering of both rows and columns of the gene expression matrix, instead of clustering these two dimensions separately To find biclusters is NP-hard problem: It is actually a generalized version of traditional clustering

Previous Work A systematic comparison and evaluation of biclustering methods for gene expression data - Amela Prelic (2006) Algorithms: ▫Statistical Algorithmic Method for Biclustering Analysis Algorithm (SAMBA) ▫Order Preserving Submatrix Algorithm (OPSM) ▫Iterative Signature Algorithm (ISA) ▫Cheng and Church algorithm ▫xMotif ▫Bimax

Previous Work (Cont.) Comparative Analysis of Biclustering Algorithms – Doruk Bozdag … (2010) Algorithms ▫Correlated Pattern Bicluster Algorithm (CPB) ▫Cheng and Church Algorithm ▫Order Preserving Submatrix Algorithm (OPSM) ▫HARP Algorithm ▫Minimum Sum-Squared Residue-based CoClustering Algorithm (MSSRCC) ▫Statistical Algorithmic Method for Biclustering Analysis Algorithm (SAMBA)

The Importance of Assessment Different algorithms give different solutions for same data There is no agreed upon guideline for choosing among them Validation Techniques ▫External Validation Measures  Evaluate a result based on the knowledge of the correct class labels ▫Internal Validation Measures  Evaluate a result based on the information intrinsic to the data alone

Validation In most biclustering papers external validation measures used to assess the methods, ▫It is not clear how to extend notions such as homogeneity and separation to the biclustering context (Gat-Viks et al 2003) ▫Internal measures don’t work well in case of biclustering due to which Gat-Viks et al 2003 and Handl et al 2005 recommend external measures

Objectives of the Project Comprehensive Assessment Technique ▫Internal measures as well as external measures Customized Biclustering Method ▫Input domain

Validation using Synthetic Data Testing using Manufactured data ▫The portion of the implanted bicluster the algorithm was able to return ▫The portion external or irrelevant to the implanted bicluster which algorithm returns ▫Two metrics to evaluate cluster quality  U: Uncovered portion of the implanted bicluster  E: Portion of the output cluster external to the implanted bicluster

Validation using Synthetic Data Testing using real (domain specific) data – for example using Gene match score ▫M1, M2 be two sets of Biclusters ▫Average of the maximum match scores for all biclusters in M1 with respect to the bicluster in M2 Potential improvements ▫Don’t consider samples / conditions ▫Specificity and Sensitivity

Proposed Assessment Calculate sensitivity and specificity scores ▫Specificity: proportion of negatives which are correctly identified ▫Sensitivity: proportion of actual positives which are correctly identified Improve existing measures: ▫Average of the maximum match scores for all bi-clusters in M1 with respect to bi-clusters in M2 (considering both genes and samples) Assessment based on knowledge of domain data ▫The resulting biclusters were evaluated based on the enrichment of Gene Ontology (GO) terms

Experiments Given two biclustering results ▫M1: Result of a biclustering algorithm ▫M2: True Result ▫(G1, C1) M1 and (G2, C2) M2 Calculate similarity score (Jaccard Coefficient) ▫ and Calculate the two scores, ▫Score 1: % of result of an algorithm is included in the true result ▫Score 2: % of true result an algorithm can find

Results Synthetic Data: 100 genes and 100 samples 10 implanted biclusters of each size 10 X 10 (10 genes and 10 samples) Used publically available different biclustering algorithm implementations Score 1: % of result of an algorithm is included in the true result Score 2: % of true result an algorithm can find Algorithm No of biclusters Score 1Score 2 Cheng and Church Algorithm (CC) Iterative Search Algorithm (ISA)910.9 Order Preserving Sub Matrix (OPSM) Algorithm Statistical Algorithm Method for Bicluster Analysis (SAMBA) xMotif Algorithm

Conclusion Traditional Clustering is too restrictive technique for analyzing datasets in various application domains We need new flexible analysis technique like biclustering to deal with possible imperfections in the input datasets Assessment of data analysis is critical and must be considered while selecting the right tool for each application domains Biclustering represents a powerful tool for analysis of data in a variety of domains and can be applicable to datasets other than biology

References Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey Amela Prelic et al: A systematic comparison and evaluation of biclustering methods for gene expression data

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