Multiple network-constrained regressions expand insights into influenza vaccination responses.
Multiple network-constrained regressions expand insights into influenza vaccination responses.
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DOI：
10.1093/bioinformatics/btx260
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发表时间：
2017-07-15
期刊：
影响因子：
--
通讯作者：
Kleinstein SH
中科院分区：
文献类型：
--
作者：
Avey S;Mohanty S;Wilson J;Zapata H;Joshi SR;Siconolfi B;Tsang S;Shaw AC;Kleinstein SH
系统免疫学利用最近的技术进步，使免疫系统能够更好地了解对感染和疫苗接种的反应，以及疾病中发生的调节失调。为了从这些大规模的图谱实验中获得洞察力，一种越来越常见的方法涉及应用统计学习方法来预测疾病状态或对扰动的免疫反应。然而，许多系统研究的目标不是最大限度地提高准确性，而是获得生物学上的洞察。使用当前方法确定的预测因素可能在生物学上无法解释，或者只提供了许多同等预测模型中的一个，导致对潜在生物学的狭隘理解。在这里，我们表明，通过使用转录图谱数据的网络级约束，将先前的生物学知识整合到Logistic建模框架中，显着提高了可解释性。此外，结合不同类型的生物学知识产生的模型突出了潜在生物学的不同方面，同时保持了预测的准确性。我们提出了一种新的框架，Logistic多重网络约束回归(Logistic Multiple Network-Constraint Regregation，LogMiNeR)，并将其应用于理解流感疫苗接种差异反应的潜在机制。虽然标准的Logistic回归方法是预测性的，但它们的可解释性很低。使用LogMiNeR整合先验知识导致了同样具有预测性但高度可解释的模型。在这种背景下，B细胞特异性基因和mTOR信号与年轻人的有效疫苗接种反应有关。总体而言，我们的结果展示了一种分析高维免疫图谱数据的新范式，其中结合了编码先验知识的多个网络以提高模型的可解释性。本文中描述的R源代码可在https://bitbucket.org/kleinstein/logminer.上公开获得补充数据可在生物信息学在线上获得。
Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer. Supplementary data are available at Bioinformatics online.
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