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Bioinformatics Advance Access published online on February 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti329
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 23, 2004
Revised January 27, 2005
Accepted February 12, 2005

Article

Sorting Points Into Neighborhoods (SPIN): data analysis and visualization by ordering distance matrices

D. Tsafrir 1*, I. Tsafrir 1, L. Ein-Dor 1, O. Zuk 1, D. A. Notterman 2, and E. Domany 1

1 Department of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel
2 Department of Pediatrics, UMDNJ-Robert Wood Johnson Medical School; Department of Molecular Genetics, UMDNJ-Robert Wood Johnson Medical School

* To whom correspondence should be addressed.
D. Tsafrir, E-mail: fedafna{at}wisemail.weizmann.ac.il


  Abstract

We introduce a novel unsupervised approach for the organization and visualization of multi-dimensional data. At the heart of the method is a presentation of the full pairwise distance matrix of the data points, viewed in pseudo-color. The ordering of points is iteratively permuted in search of a linear ordering, which can be used to study embedded shapes. Several examples indicate how the shapes of certain structures in the data (elongated, circular and compact) manifest themselves visually in our permuted distance matrix. It is important to identify elongated objects since they are often associated with a set of hidden variables, underlying continuous variation in the data. The problem of determining an optimal linear ordering is shown to be NP complete, and therefore an iterative search algorithm with O(n3) step-complexity is suggested. By using SPIN to analyze colon cancer expression data we were able to address the serious problem of sample heterogeneity, which hinders identification of metastasis related genes in our data. Our methodology brings to light the continuous variation of heterogeneity - starting with homogeneous tumor samples and gradually increasing the amount of another tissue. Ordering the samples according to their degree of contamination by unrelated tissue allows separation of genes associated with irrelevant contamination from those related to cancer progression.

Availability: Software package will be available for academic users upon request.


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