BACKGROUND To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. METHODS Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. RESULTS In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. CONCLUSIONS We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

中文翻译：

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改进的大量基因表达谱的细胞组成反卷积方法，用于定量免疫细胞的亚群。

背景技术为了促进对诸如肿瘤之类的复杂组织中的各种免疫细胞所起的致病作用的研究，已经创建了一些用于对大体积基因表达谱进行去卷积以预测细胞组成的计算方法。但是，通常会开发出可用的方法以及一组参考基因表达谱，其中包括跨不同细胞类型的不平衡重复。因此，这项研究的目的是创建一种新的反卷积方法，该方法配备了一套新的参考基因表达谱，其中包含了更多的免疫细胞微阵列复制品，这些复制品经常与癌症的不良预后有关，例如T辅助细胞，调节性T细胞和巨噬细胞M1 / M2细胞。方法我们的反卷积方法是通过选择ε-支持向量回归（ε-SVR）作为分配有L1-norm惩罚的损失函数的核心算法而开发的。为了构建用于回归的参考基因表达特征矩阵，通过使用ANOVA和最小化条件数从148种基于微阵列的基因表达谱中选择了9种类型免疫细胞的差异表达基因的一个子集。进行了包括平均绝对百分比误差和Bland-Altman图在内的一致性分析，以比较我们的方法和CIBERSORT的性能。结果在计算机细胞混合物，模拟的大块组织和具有已知免疫细胞分数的真实人类样品中，将其用作基准测试的测试数据集。我们的方法使用比例为30:70和50:50的计算机乳腺组织免疫细胞混合物的基准性能优于CIBERSORT，而使用164种人PBMC样品的基准性能优于CIBERSORT。我们的结果表明，我们方法的性能至少可以与最先进的工具CIBERSORT相比。结论我们开发了一种新的单元组成反卷积方法，其实现完全基于可公开获得的R和Python软件包。此外，我们编辑了一组新的参考基因表达谱，这可能允许根据细胞混合物的表达谱对免疫细胞组分进行更可靠的预测。我们方法的源代码可以从https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets下载。并且在基准测试中使用了164个人类PBMC样本。我们的结果表明，我们方法的性能至少可以与最先进的工具CIBERSORT相比。结论我们开发了一种新的单元组成反卷积方法，其实现完全基于可公开获得的R和Python软件包。此外，我们编辑了一组新的参考基因表达谱，这可能允许根据细胞混合物的表达谱对免疫细胞组分进行更可靠的预测。我们方法的源代码可以从https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets下载。并且在基准测试中使用了164个人类PBMC样本。我们的结果表明，我们方法的性能至少可以与最先进的工具CIBERSORT相比。结论我们开发了一种新的单元组成反卷积方法，其实现完全基于可公开获得的R和Python软件包。此外，我们编辑了一组新的参考基因表达谱，这可能允许根据细胞混合物的表达谱对免疫细胞组分进行更可靠的预测。我们方法的源代码可以从https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets下载。结论我们开发了一种新的单元组成反卷积方法，其实现完全基于可公开获得的R和Python软件包。此外，我们编辑了一组新的参考基因表达谱，这可能允许根据细胞混合物的表达谱对免疫细胞组分进行更可靠的预测。我们方法的源代码可以从https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets下载。结论我们开发了一种新的单元组成反卷积方法，其实现完全基于可公开获得的R和Python软件包。此外，我们编辑了一组新的参考基因表达谱，这可能允许根据细胞混合物的表达谱对免疫细胞组分进行更可靠的预测。我们方法的源代码可以从https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets下载。