利用循环计数技术检测布尔不对称关系及其对分析基因表达数据集异质性的影响

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-29 DOI:10.1109/TCBB.2024.3487434
Haosheng Zhou, Wei Lin, Sergio R Labra, Stuart A Lipton, Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan
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引用次数: 0

摘要

许多分析基因-基因关系的传统方法都侧重于正相关和负相关,这两种关系都是一种 "对称 "关系。双聚类就是这样一种技术,它通常在样本子集中搜索表现出相关表达的基因子集。然而,基因也可以表现出 "非对称 "关系,例如布尔电路中使用的 "如果-那么 "关系。在本文中,我们开发了一种非常通用的方法,可用于检测基因表达数据中的双簇,这些数据涉及富集了这些 "布尔-非对称 "关系(BAR)的基因子集。这些 "布尔-非对称 "关系双集群可能对应于由非对称基因-基因相互作用驱动的异质性,例如,反映一个基因对另一个基因的调控作用,而不是更标准的对称相互作用。与在整个群体中搜索 BAR 的典型方法不同,BAR-双簇可以检测到只发生在部分样本中的非对称相互作用。我们将这一方法应用于单细胞 RNA 序列数据集,结果表明,在统计意义上显著的 BAR 双簇确实包含了更传统的 "布尔-对称 "双簇所不具备的额外信息。例如,BAR 双簇涉及不同的细胞子集,并突出了数据集中不同的基因通路。此外,通过结合布尔-非对称信号和布尔-对称信号,我们可以建立线性分类器,其效果优于仅使用传统布尔-对称信号建立的分类器。
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Detecting Boolean Asymmetric Relationships with a Loop Counting Technique and its Implications for Analyzing Heterogeneity within Gene Expression Datasets.

Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of 'symmetric' relationship. Biclustering is one such technique that typically searches for subsets of genes exhibiting correlated expression among a subset of samples. However, genes can also exhibit 'asymmetric' relationships, such as 'if-then' relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these 'boolean-asymmetric' relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, e.g., reflecting regulatory effects of one gene on another, rather than more standard symmetric interactions. Unlike typical approaches that search for BARs across the entire population, BAR-biclusters can detect asymmetric interactions that only occur among a subset of samples. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BARbiclusters indeed contain additional information not present within the more traditional 'boolean-symmetric'-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetricsignals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.

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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
期刊最新文献
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