ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition
ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition
复制标题
ChIP-PIT：使用凸松弛成对相互作用张量分解增强 ChIP-Seq 数据的分析
DOI：
10.1109/tcbb.2015.2465893
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Huang De-Shuang
中科院分区：
文献类型：
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作者：
Zhu Lin;Guo Wei-Li;Deng Su-Ping;Huang De-Shuang
近年来，由于个别科学家和研究财团的努力，积累了大量的染色质免疫沉淀随后高通量测序（ChIP-seq）实验数据。最近的几项研究令人信服地证明，通过对这些ChIP-seq数据的综合分析，可以获得丰富的科学见解，而不是独立地调查它们。然而，当用于综合分析时，当前ChIP-seq技术的一个严重缺点是生成高标准的ChIP-seq数据集仍然昂贵且耗时。因此，大多数研究人员无法获得多种细胞系中几种TF的完整ChIP-seq数据，这大大限制了对转录调控模式的理解。在本文中，我们提出了一种称为ChIP-PIT的新方法来克服上述限制。在ChIP-PIT中，使用三模式成对相互作用张量（PIT）模型将对应于不同细胞类型、TF和基因集合的ChIP-seq数据融合在一起，并且将未执行的ChIP-seq实验结果的预测公式化为张量完成问题。在计算上，我们提出了基于坐标下降法扩展的高效一阶方法来学习ChIP-PIT的最优解，这使得它特别适合于大规模ChIP-seq数据的分析。ENCODE数据的实验评估说明了所提出的模型的实用性。
In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigating them independently, several recent studies have convincingly demonstrated that a wealth of scientific insights can be gained by integrative analysis of these ChIP-seq data. However, when used for the purpose of integrative analysis, a serious drawback of current ChIP-seq technique is that it is still expensive and time-consuming to generate ChIP-seq datasets of high standard. Most researchers are therefore unable to obtain complete ChIP-seq data for several TFs in a wide variety of cell lines, which considerably limits the understanding of transcriptional regulation pattern. In this paper, we propose a novel method called ChIP-PIT to overcome the aforementioned limitation. In ChIP-PIT, ChIP-seq data corresponding to a diverse collection of cell types, TFs and genes are fused together using the three-mode pair-wise interaction tensor (PIT) model, and the prediction of unperformed ChIP-seq experimental results is formulated as a tensor completion problem. Computationally, we propose efficient first-order method based on extensions of coordinate descent method to learn the optimal solution of ChIP-PIT, which makes it particularly suitable for the analysis of massive scale ChIP-seq data. Experimental evaluation the ENCODE data illustrate the usefulness of the proposed model.