在微阵列数据的超顺磁性聚类中包含转录因子信息
Including transcription factor information in the superparamagnetic clustering of microarray data
摘要：
在这项工作中，我们修改了超顺磁性聚类算法（SPC），增加了一个额外的权重的相互作用公式，认为哪些基因是由相同的转录因子。使用我们称之为SPCTF的改进算法，我们分析了Spellman等人的酵母细胞周期基因微阵列数据，并发现与SPC算法相比具有更多元素的簇。Spellman等人最初没有检测到通过使用SPCFT掺入的一些基因，但后来被其他研究鉴定，而一些基因仍然未分类。用MUSA（motif finding）分析未鉴定基因组成的簇，使用无监督的方法算法发现基序，这使我们能够选择其元件包含细胞周期转录因子结合位点的簇作为值得进一步实验研究的簇，因为它们可能导致新的细胞周期基因。最后，我们的思想，引入可用的信息，转录因子，以优化基因分类可以实现其他基于距离的聚类算法
英文摘要：
In this work, we modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. With this modified algorithm that we call SPCTF, we analyze Spellman et al. microarray data for cell cycle genes in yeast, and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worth of further experimental studies because they would probably lead to new cell cycle genes. Finally, our idea of introducing available information about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms
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