کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417226 681468 2008 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Assessing agreement of clustering methods with gene expression microarray data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Assessing agreement of clustering methods with gene expression microarray data
چکیده انگلیسی

In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face the choice of which clustering algorithm(s) to use and how to interpret different results from various clustering algorithms. No clear objective criteria have been developed to assess the agreement and compare the results from different clustering methods. We describe two generally applicable objective measures to quantify agreement between different clustering methods. These two measures are referred to as the local agreement measure, which is defined for each gene/subject, and the global agreement measure, which is defined for the whole gene expression experiment. The agreement measures are based on a probabilistic weighting scheme applied to the number of concordant and discordant pairs from two clustering methods. In the comparison and assessment process, newly-developed concepts are implemented under the framework of reliability of a cluster. The algorithms are illustrated by simulations and then applied to a yeast sporulation gene expression microarray data. Analysis of the sporulation data identified ∼5% (23 of 477) genes which were not consistently clustered using a neural net algorithm and K-means or pam. The two agreement measures provide objective criteria to conclude whether or not two clustering methods agree with each other. Using the local agreement measure, genes of unknown function which cluster consistently can more confidently be assigned functions based on co-regulation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 12, 15 August 2008, Pages 5356–5366
نویسندگان
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