Identification of cancer genomic markers via integrative sparse boosting
Identification of cancer genomic markers via integrative sparse boosting
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DOI：
10.1093/biostatistics/kxr033
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发表时间：
2012-07-01
期刊：
影响因子：
2.1
通讯作者：
Ma, Shuangge
中科院分区：
文献类型：
--
作者：
Huang, Yuan;Huang, Jian;Ma, Shuangge
在高通量癌症基因组研究中，由于样本量小，从单个数据集的分析中鉴定的标记通常缺乏重现性。理想的解决方案是进行大规模的前瞻性研究，这是非常昂贵和耗时的。一个具有成本效益的补救办法是汇集多项可比研究的数据，并进行综合分析。由于基因组测量的高维性和研究之间的异质性，多个数据集的综合分析具有挑战性。在这篇文章中，我们提出了一种稀疏增强方法，用于在多个异质性癌症诊断研究与基因表达测量的综合分析中进行标记物识别。所提出的方法可以有效地适应多个研究之间的异质性，并确定具有一致的影响，跨研究的标志物。仿真结果表明，该方法具有令人满意的识别结果，并优于替代品，包括强度的方法和荟萃分析。所提出的方法用于识别胰腺癌和肝癌的标志物。
In high-throughput cancer genomic studies, markers identified from the analysis of single data sets often suffer a lack of reproducibility because of the small sample sizes. An ideal solution is to conduct large-scale prospective studies, which are extremely expensive and time consuming. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple data sets is challenging because of the high dimensionality of genomic measurements and heterogeneity among studies. In this article, we propose a sparse boosting approach for marker identification in integrative analysis of multiple heterogeneous cancer diagnosis studies with gene expression measurements. The proposed approach can effectively accommodate the heterogeneity among multiple studies and identify markers with consistent effects across studies. Simulation shows that the proposed approach has satisfactory identification results and outperforms alternatives including an intensity approach and meta-analysis. The proposed approach is used to identify markers of pancreatic cancer and liver cancer.