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题目:: 19 K14Cre; p53F/F mouse mammary tumor analysis
ID:
状态:: 发布时间May 26, 2010 , 更新时间 May 2, 2014 , 提交时间 May 13, 2007,
物种:: Mus musculus
摘要:: Tumor formation is in part driven by copy number alterations (CNAs), which can be measured using array Comparative Genomic Hybridization (aCGH). Identifying regions of DNA that are gained or lost in a significant fraction of tumor samples can facilitate identification of genes possibly related to the development of cancer. Until now, no method has been described that provides a statistical framework in which these regions can be identified without prior discretization of the aCGH data. Kernel Convolution - a Statistical Method for Aberrant Region deTection (KC-SMART) is a new approach which inputs continuous aCGH data to identify regions that are significantly aberrant across an entire tumor set. KC-SMART uses kernel convolution to generate a Kernel Smoothed Estimate (KSE) of CNAs across the genome, aggregated over all tumors. By varying the width of the kernel function, a scale space is created which enables the detection of aberrations of varying size. In an analysis of 89 human sporadic breast tumors KC-SMART performs better than a previously published method, STAC. Our method not only identified aberrations that are strongly associated with clinical parameters, but also showed stronger enrichment for known cancer genes in the detected regions. Furthermore, KC-SMART identifies 18 aberrant regions in mammary tumors from p53 conditional knock-out mice. These regions, combined with gene expression micro-array data, point to known cancer genes and novel candidate cancer genes. 19 mouse mammary tumors samples were measured against spleen-derived DNA from the same animal on our in-house aCGH platform. Goal of the study was to assess recurrent genomic aberrations in these tumors. This is a tissue specific knockout of p53. Experiments were perfomed in dye-swap
实验种类:: unknown experiment type
样本量:: 76
实验设计:
: 无设计数据
数据号:: E-GEOD-7794, GSE7794
数据状态:: 无法自动分析，您可以尝试手动分析数据。

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