平铺阵列的无监督分类：ChIP-chip和Transcriptome
Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome
摘要：
平铺阵列使基因组的大规模探索成为可能，这要归功于探针以非常高的密度覆盖整个基因组，直到200万个探针。通常解决的生物学问题是两种条件之间的表达差异或转录区域的检测。在这项工作中，我们建议同时考虑这两个问题作为一个无监督的分类问题建模的联合分布的两个条件。与以前的方法相比，我们考虑了探针上的所有可用信息以及生物学知识，如探针之间的注释和空间依赖性。由于探针不是生物相关的单位，我们提出了一个分类规则，由几个探针覆盖的非连接区域。应用转录组学和ChIP芯片数据的拟南芥获得了一个NimbleGen平铺阵列突出了精确建模和区域分类的重要性。
英文摘要：
Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
