Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Cell Syst. 2024 Sep 18;15(9):854-868.e3. doi: 10.1016/j.cels.2024.08.005. Epub 2024 Sep 6.
Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.
识别数据中的有意义模式对于理解复杂的生物学过程至关重要，特别是在转录组学中，具有相关表达的基因通常具有相似的功能或有助于疾病机制。传统的相关系数主要捕捉线性关系，可能会忽略重要的非线性模式。我们引入了 clustermatch 相关系数（CCC），这是一种不仅线性的系数，它利用聚类来有效地检测线性和非线性关联。CCC 通过揭示线性系数遗漏的生物学有意义的模式，并且比最先进的系数（如最大信息系数）更快，从而优于标准方法。当应用于来自基因型-组织表达（GTEx）的人类基因表达数据时，CCC 识别出了稳健的线性关系和非线性模式，例如性别特异性差异，这是标准方法无法检测到的。排名靠前的基因对在从蛋白质-蛋白质相互作用、转录因子调控以及化学和遗传扰动构建的综合网络中的相互作用富集，表明 CCC 可以检测到线性方法遗漏的功能关系。CCC 是一种高效、下一代、不仅线性的基因组规模数据相关系数。本文的透明同行评审过程记录包含在补充信息中。
