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An Algorithm for Clustering cDNAs for Gene Expression Analysis
 In RECOMB99: Proceedings of the Third Annual International Conference on Computational Molecular Biology
, 1999
"... We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a clusterin ..."
Abstract

Cited by 45 (4 self)
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We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a clustering with some provably good properties. The application that motivated this study was gene expression analysis, where a collection of cDNAs must be clustered based on their oligonucleotide fingerprints. The algorithm has been tested intensively on simulated libraries and was shown to outperform extant methods. It demonstrated robustness to high noise levels. In a blind test on real cDNA fingerprint data the algorithm obtained very good results. Utilizing the results of the algorithm would have saved over 70% of the cDNA sequencing cost on that data set. 1 Introduction Cluster analysis seeks grouping of data elements into subsets, so that elements in the same subset are in some sense more cl...
Construction of Physical Maps From Oligonucleotide Fingerprints Data
 J. of Computational Biology
, 1999
"... A new algorithm for the construction of physical maps from hybridization fingerprints of short oligonucleotide probes has been developed. Extensive simulations in highnoise scenarios show that the algorithm produces an essentially completely correct map in over 95% of trials. Tests for the infl ..."
Abstract

Cited by 9 (3 self)
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A new algorithm for the construction of physical maps from hybridization fingerprints of short oligonucleotide probes has been developed. Extensive simulations in highnoise scenarios show that the algorithm produces an essentially completely correct map in over 95% of trials. Tests for the influence of specific experimental parameters demonstrate that the algorithm is robust to both false positive and false negative experimental errors. The algorithm was also tested in simulations using real DNA sequences of E. coli, B. subtilis, M. tuberculosis, S. cerevisiae, C. elegans, and H. sapiens. To overcome the nonrandomness of probe frequencies in these sequences, probes were preselected based on sequence statistics and a screening process of the hybridization data was developed. With these modifications, the algorithm produced very encouraging results. A preliminary version of the paper is to appear in Proc. RECOMB 99. y Department of Computer Science, Sackler Faculty of Ex...
A Robust Algorithm for Constructing Physical Maps From Noisy NonUnique Probes Fingerprints
, 1998
"... A practical algorithm for construction of physical maps based on hybridization fingerprint data of short (nonunique) oligonucleotide probes has been developed and extensively tested. Extensive experiments with realistic, highnoise simulated data show that in over 95% of the simulations, the algori ..."
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A practical algorithm for construction of physical maps based on hybridization fingerprint data of short (nonunique) oligonucleotide probes has been developed and extensively tested. Extensive experiments with realistic, highnoise simulated data show that in over 95% of the simulations, the algorithm creates an essentially completely correct map. The influence of specific experimental parameters has also been tested, demonstrating strong robustness to both false positive and false negative experimental errors. Contents 1 Introduction 2 2 The statistical model 7 3 The Bayesian overlap score 9 3.1 Clone Pairs Overlap Score . . . . . . . . . . . . . . . . . . . . 9 3.1.1 Complexity . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Contig Pairs Overlap Score . . . . . . . . . . . . . . . . . . . 12 3.2.1 Complexity . . . . . . . . . . . . . . . . . . . . . . . . 12 4 The construction algorithm 13 4.1 The Basic Algorithm . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Improvem...