A statistical framework for expression-based molecular classification in cancer
A statistical framework for expression-based molecular classification in cancer
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DOI：
10.1111/1467-9868.00358
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发表时间：
2002-01-01
影响因子：
5.8
通讯作者：
Gabrielson, E
中科院分区：
文献类型：
--
作者：
Parmigiani, G;Garrett, ES;Gabrielson, E
基因表达的全基因组测量是一种很有前途的方法，目前无法区分，但潜在的生物学异质性的癌症亚型的鉴定。这种类型的分子分类为高度个性化和更有效的癌症预后和治疗带来了希望。从统计学上讲，分析来自未分类肿瘤的基因表达数据是一项复杂的假设生成活动，涉及数据探索，建模和专家启发。我们提出了一个建模框架，可用于通知和组织开发的探索性工具分类。我们的框架使用潜在的类别，以提供一个统计定义的差异表达和一个精确的，实验无关的，定义的分子概况。它还生成传统聚类的自然相似性度量，并给出关于肿瘤分子谱分配的概率陈述。
Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.