• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of VLSI signal processing systems for signal, image, and video technology >A Comparison Of Fuzzy Clustering Approaches For Quantification Of Microarray Gene Expression
【24h】

A Comparison Of Fuzzy Clustering Approaches For Quantification Of Microarray Gene Expression

机译：模糊聚类方法定量分析微阵列基因表达的比较

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.
机译：尽管微阵列成像在生物医学成像研究中得到了广泛的应用，但其在临床上的可靠性仍然存在障碍。关键的主要问题在于精确的点分割和微阵列图像中基因表达水平（mRNA）的定量。可以使用多种商业和研究免费软件包，但是大多数软件包无法处理形状复杂的阵列点，例如甜甜圈和划痕。为了克服此困难，引入了聚类方法（例如k均值和混合模型），该方法使用每个像素的硬标签。在本文中，我们将模糊聚类方法应用于点分割，从而提供了像素的软标记。我们比较了几种模糊聚类方法的微阵列分析，并提供了这些方法的斑点分割的全面研究。我们显示，在对各种模拟和真实微阵列图像进行测试时，就稳定性标准而言，可能的c均值聚类（PCM）可提供最佳性能。此外，我们在测量基因表达水平时比较了三种统计标准，并表明新的渐近无偏统计量能够更准确地量化基因表达水平。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号