标题： 用于分析大规模基因表达数据的迭代签名算法 显示英文标题

标题： The Iterative Signature Algorithm for the analysis of large scale gene expression data

摘要： 我们提出了一种新的方法来分析全基因组表达数据。 我们的方法旨在克服传统技术在应用于大规模数据时的局限性。 而不是将每个基因分配到一个单一的聚类，我们将基因和条件分配到上下文依赖且可能重叠的转录模块。 我们提供了转录模块的严格定义，作为从表达数据中检索的对象。 建立了一个高效的算法，该算法通过迭代地优化基因和条件的集合，直到它们符合这一定义，从而搜索数据中编码的模块。 每次迭代包括由归一化表达矩阵引起的线性映射，然后应用阈值函数。 我们认为，我们的方法实际上是奇异值分解的一种推广，这对应于不应用阈值的特殊情况。 我们通过分析噪声表达数据证明，由于实现了阈值，我们的方法会导致更好的分类。 这一结果通过基于模拟表达数据的数值分析得到证实。 我们简要讨论了将我们的算法应用于酵母S. cerevisiae表达数据所得的结果。 显示英文摘要

摘要： We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.