HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression-猫眼课题宝

DOI：

10.48130/fr-2021-0006

发表时间：

2021

期刊：

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其他

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作者：

Wenping Deng;Kui Zhang;Cheng He;Sanzhen Liu;Hairong Wei

基因表达数据具有高维数、多重共线性和非高斯分布噪声的特点，这给识别控制生物过程或途径的真正调控基因带来了障碍。针对基因表达数据的高维性和多重共线性，将Huber损失函数和Berhu罚函数（HB）结合到偏最小二乘（PLS）框架中，提出了HB-PLS回归模型，用于调控基因和途径基因之间的关系建模。为了解决Huber-Berhu优化问题，开发了一种加速近似梯度下降算法，其速度至少是一般凸优化求解器（CVX）的10倍。应用HB-PLS识别拟南芥中木质素生物合成和光合作用的途径调节剂，鉴定了许多已知的阳性途径调节剂，这些调节剂以前已经过实验验证。与稀疏偏最小二乘（SPLS）回归（一种用于处理多重共线性中的变量选择和降维的有效方法）相比，HB-PLS在鉴定更阳性的已知调节子方面具有更高的功效，在将真阳性已知调节子排序到上述两种途径的输出调节基因列表的顶部方面具有高得多但略低的灵敏度/（1-特异性）。此外，每种方法都可以识别一些其他方法无法识别的独特调节剂。结果表明，HBPLS的整体性能略优于SPLS，但两种方法都能从高通量基因表达数据中识别出真实的通路调控因子，表明统计、机器学习和凸优化相结合的方法具有较高的效率，值得进一步探索。引文：邓伟，张克，何春，刘S，魏华。2021. HB-PLS：一种通过将Huber损失和Berhu罚分与偏最小二乘回归相结合来识别生物过程或途径调节剂的统计方法。林业研究1：6 https://doi.org/10.48130/FR-2021-0006

Gene expression data features high dimensionality, multicollinearity, and non-Gaussian distribution noise, posing hurdles for identification of true regulatory genes controlling a biological process or pathway. In this study, we integrated the Huber loss function and the Berhu penalty (HB) into partial least squares (PLS) framework to deal with the high dimension and multicollinearity property of gene expression data, and developed a new method called HB-PLS regression to model the relationships between regulatory genes and pathway genes. To solve the Huber-Berhu optimization problem, an accelerated proximal gradient descent algorithm with at least 10 times faster than the general convex optimization solver (CVX), was developed. Application of HB-PLS to recognize pathway regulators of lignin biosynthesis and photosynthesis in Arabidopsis thaliana led to the identification of many known positive pathway regulators that had previously been experimentally validated. As compared to sparse partial least squares (SPLS) regression, an efficient method for variable selection and dimension reduction in handling multicollinearity, HB-PLS has higher efficacy in identifying more positive known regulators, a much higher but slightly less sensitivity/(1-specificity) in ranking the true positive known regulators to the top of the output regulatory gene lists for the two aforementioned pathways. In addition, each method could identify some unique regulators that cannot be identified by the other methods. Our results showed that the overall performance of HBPLS slightly exceeds that of SPLS but both methods are instrumental for identifying real pathway regulators from high-throughput gene expression data, suggesting that integration of statistics, machine leaning and convex optimization can result in a method with high efficacy and is worth further exploration. Citation: Deng W, Zhang K, He C, Liu S, Wei H. 2021. HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression. Forestry Research 1: 6 https://doi.org/10.48130/FR-2021-0006

HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression

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