An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets
An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets
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DOI：
10.1186/s12859-019-2994-z
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发表时间：
2019-08-22
影响因子：
3
通讯作者：
Klinke, David J., II
中科院分区：
文献类型：
--
作者：
Torang, Arezo;Gupta, Paraag;Klinke, David J., II
背景：宿主的免疫反应是由各种不同的特殊细胞类型协调的，这些细胞类型在时间和位置上各不相同。虽然可以使用传统的低维方法研究宿主免疫反应，但转录组分析的进展可能提供一个较少偏见的观点。然而，利用转录组数据来识别免疫细胞亚型为提取隐藏在高维转录组空间中的信息基因特征带来了挑战，高维转录组空间的特征是样本数量低，具有噪声和缺失值。为了应对这些挑战，我们探索了使用机器学习方法来选择基因亚集和估计基因系数的方法。结果：采用机器学习的弹性网络Logistic回归方法，对10种不同类型的免疫细胞和5种辅助T细胞亚集分别构建了分类器。然后，使用得到的分类器来开发使用RNA-SEQ数据集在免疫细胞类型和T辅助细胞亚群之间进行最佳区分的基因特征。我们使用单细胞RNA-seq(scRNA-seq)数据集验证了该方法，给出了一致的结果。此外，我们还对以前未加注释的细胞类型进行了分类。结论：所开发的分类器可以作为预测疾病中宿主免疫反应的范围和功能定位的先验，例如癌症，在这些疾病中，大量组织样本和单个细胞的转录转录图谱是常规的。能够洞察疾病的机制基础和治疗反应的信息。源代码和文档可通过GitHub获得：https://github.com/KlinkeLab/ImmClass2019.
Background: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously.Results: Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures.Conclusions: Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019.