Bayesian clustering and feature selection for cancer tissue samples.
Bayesian clustering and feature selection for cancer tissue samples.
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DOI：
10.1186/1471-2105-10-90
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发表时间：
2009-03-18
影响因子：
3
通讯作者：
Corander J
中科院分区：
文献类型：
--
作者：
Marttinen P;Myllykangas S;Corander J
DNA拷贝数扩增用于各种组织样品的分析和分类的多功能性已在生物医学文献中得到广泛认可。例如，这种类型的测量技术提供了探索癌组织组以识别新亚型的可能性。以前使用的各种分析的统计方法包括传统的算法技术的聚类和降维，如独立和主成分分析，层次聚类，以及基于模型的聚类使用最大似然估计的潜在类模型。虽然纯算法方法通常很容易应用，但它们在进行正式推理时的次优性能和局限性已经在统计文献中进行了彻底的讨论。在这里，我们介绍了贝叶斯模型为基础的方法，同时识别潜在的组织组和信息的扩增。基于模型的方法提供了使用正式推理来确定数据中的组数的可能性，与经常用于类似目的的特设方法相反。该模型还自动识别与聚类相关的染色体区域。验证分析的模拟数据和一个大型数据库的DNA拷贝数扩增在人类肿瘤被用来说明我们的方法的潜力。我们用于执行贝叶斯统计组织分析的软件实现BASTA可免费用于学术目的，
The versatility of DNA copy number amplifications for profiling and categorization of various tissue samples has been widely acknowledged in the biomedical literature. For instance, this type of measurement techniques provides possibilities for exploring sets of cancerous tissues to identify novel subtypes. The previously utilized statistical approaches to various kinds of analyses include traditional algorithmic techniques for clustering and dimension reduction, such as independent and principal component analyses, hierarchical clustering, as well as model-based clustering using maximum likelihood estimation for latent class models. While purely algorithmic methods are usually easily applicable, their suboptimal performance and limitations in making formal inference have been thoroughly discussed in the statistical literature. Here we introduce a Bayesian model-based approach to simultaneous identification of underlying tissue groups and the informative amplifications. The model-based approach provides the possibility of using formal inference to determine the number of groups from the data, in contrast to the ad hoc methods often exploited for similar purposes. The model also automatically recognizes the chromosomal areas that are relevant for the clustering. Validatory analyses of simulated data and a large database of DNA copy number amplifications in human neoplasms are used to illustrate the potential of our approach. Our software implementation BASTA for performing Bayesian statistical tissue profiling is freely available for academic purposes at
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影响因子：
2.2
作者：
Corander, Jukka;Gyllenberg, Mats;Koski, Timo
通讯作者：
Koski, Timo
DOI：
10.1080/01621459.1995.10476590
发表时间：
1995-09-01
影响因子：
3.7
作者：
GEYER, CJ;THOMPSON, EA
通讯作者：
THOMPSON, EA
影响因子：
1.6
作者：
Gyllenberg, M;Koski, T;Verlaan, M
通讯作者：
Verlaan, M
影响因子：
2.4
作者：
Neal, RM
通讯作者：
Neal, RM
影响因子：
3.5
作者：
Corander, Jukka;Gyllenberg, Mats;Koski, Timo
通讯作者：
Koski, Timo