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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets
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Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets

机译：半监督拓扑分析，用于阐明高维转录组数据集中的隐藏结构

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摘要

Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to 1-dimensional space by using a filter function that is subsequently used to reconstruct the data topology relationships. However, domain context information and prior knowledge have not been considered in current TDA modeling frameworks. Here, we report the development and evaluation of a semi-supervised topological analysis (STA) framework that incorporates discrete or continuously labeled data points and selects the most relevant filter functions accordingly. We validate the proposed STA framework with simulation data and then apply it to samples from Genotype-Tissue Expression data and ovarian cancer transcriptome datasets. The graphs generated by STA for these 2 datasets, based on gene expression profiles, are consistent with prior knowledge, thereby supporting the effectiveness of the proposed framework.
机译：拓扑数据分析（TDA）是减少数据维度，挖掘底层数据关系的强大方法，直观地表示数据结构。 Mapper算法是一种这样的工具，它通过使用随后用于重建数据拓扑关系的滤波器函数将高维数据投影为1维空间。但是，在当前TDA建模框架中尚未考虑域上下文信息和先验知识。在这里，我们报告了半监督拓扑分析（STA）框架的开发和评估，该分析包含离散或连续标记的数据点，并相应地选择最相关的过滤器功能。我们通过模拟数据验证所提出的STA框架，然后将其应用于来自基因型组织表达数据和卵巢癌转录组数据集的样本。基于基因表达轮廓的STA为这2个数据集产生的图表与先验知识一致，从而支持所提出的框架的有效性。

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