CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data
CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data
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DOI：
10.1371/journal.pcbi.1007510
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发表时间：
2019-12-01
影响因子：
4.3
通讯作者：
Li, Leping
中科院分区：
文献类型：
--
作者：
Kang, Kai;Meng, Qian;Li, Leping
作者摘要 了解大块组织的细胞组成对于研究许多生物过程的潜在机制至关重要。单细胞测序是一种很有前途的技术，但它价格昂贵，而且单细胞数据的分析也很重要。因此，组织样本仍然常规地进行批量处理。为了使用大量基因表达数据估计细胞类型组成，需要计算反卷积方法。已经提出了许多反卷积方法，然而，它们通常仅使用参考细胞类型基因表达谱来估计细胞类型比例，这在许多情况下可能不可用。我们提出了一种新颖的完整反卷积方法，该方法仅使用大量基因表达数据来同时估计细胞类型特异性基因表达谱和样本特异性细胞类型比例。我们表明，使用先前已知细胞类型组成的多个 RNA-Seq 和微阵列数据集，我们的方法可以准确确定细胞类型组成。通过提供一种需要单一输入来确定细胞类型比例和细胞类型特异性表达谱的方法，我们希望我们的方法将有益于生物学家，并促进许多生物过程背后机制的研究和识别。量化组织样本中的细胞类型比例及其相应的基因表达谱将增强对单个细胞类型对组织生理状态的贡献的理解。当前解决组织异质性的方法存在缺陷。荧光激活细胞分选和单细胞 RNA 测序等实验技术非常昂贵。使用来自异质样本的表达数据的计算方法是有前景的，但当前的大多数方法通过需要另一个作为输入来估计细胞类型比例或细胞类型特异性表达谱。尽管这种部分反卷积方法已成功应用于肿瘤样本，但所需的额外输入可能无法获得。我们引入了一种新颖的完全反卷积方法 CDSeq，该方法仅使用来自大量组织样本的 RNA-Seq 数据来同时估计细胞类型比例和细胞类型特异性表达谱。使用多个具有已知细胞类型组成和细胞类型特异性表达谱的合成和真实实验数据集，我们将 CDSeq 的完整反卷积性能与其他七种已建立的反卷积方法进行了比较。使用 CDSeq 的完全反卷积代表了相对于部分反卷积方法的重大技术进步，并将有助于研究组织样本中的细胞混合物。 CDSeq 可在 GitHub 存储库中获取（MATLAB 和 Octave 代码）：.
Author summary Understanding the cellular composition of bulk tissues is critical to investigate the underlying mechanisms of many biological processes. Single cell sequencing is a promising technique, however, it is expensive and the analysis of single cell data is non-trivial. Therefore, tissue samples are still routinely processed in bulk. To estimate cell-type composition using bulk gene expression data, computational deconvolution methods are needed. Many deconvolution methods have been proposed, however, they often estimate only cell type proportions using a reference cell type gene expression profile, which in many cases may not be available. We present a novel complete deconvolution method that uses only bulk gene expression data to simultaneously estimate cell-type-specific gene expression profiles and sample-specific cell-type proportions. We showed that, using multiple RNA-Seq and microarray datasets where the cell-type composition was previously known, our method could accurately determine the cell-type composition. By providing a method that requires a single input to determine both cell-type proportion and cell-type-specific expression profiles, we expect that our method will be beneficial to biologists and facilitate the research and identification of mechanisms underlying many biological processes.Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable. We introduce a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, we compared CDSeq's complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples. CDSeq is available at GitHub repository (MATLAB and Octave code): .