Li Huamei, Sharma Amit, Luo Kun, Qin Zhaohui S, Sun Xiao, Liu Hongde
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Department of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Front Genet. 2020 May 8;11:392. doi: 10.3389/fgene.2020.00392. eCollection 2020.
While our understanding of cellular and molecular processes has grown exponentially, issues related to the cell microenvironment and cellular heterogeneity have sparked a new debate concerning the cell identity. Cell composition (chromatin and nuclear architecture) poses a strong risk for dynamic changes in the diseased condition. Since chromatin accessibility patterns play a major role in human diseases, it is therefore anticipated that a deconvolution tool based on open chromatin data will provide better performance in identifying cell composition. Herein, we have designed the deconvolution tool "DeconPeaker," which can precisely define the uniqueness among subpopulations of cells using open chromatin datasets. Using this tool, we simultaneously evaluated chromatin accessibility and gene expression datasets to estimate cell types and their respective proportions in a mixture of samples. In comparison to other known deconvolution methods, we observed the lowest average root-mean-square error (RMSE = 0.042) and the highest average correlation coefficient ( = 0.919) between the prediction and "true" proportion. As a proof-of-concept, we also tested chromatin accessibility data from acute myeloid leukemia (AML) and successfully obtained unique cell types associated with AML progression. Furthermore, we showed that chromatin accessibility represents more essential characteristics in the identification of cell types than gene expression. Taken together, DeconPeaker as a powerful tool has the potential to combine different datasets (primarily, chromatin accessibility and gene expression) and define different cell types in mixtures. The Python package of DeconPeaker is now available at https://github.com/lihuamei/DeconPeaker.
尽管我们对细胞和分子过程的理解呈指数级增长，但与细胞微环境和细胞异质性相关的问题引发了一场关于细胞身份的新辩论。细胞组成（染色质和核结构）在疾病状态下存在动态变化的巨大风险。由于染色质可及性模式在人类疾病中起主要作用，因此预计基于开放染色质数据的反卷积工具在识别细胞组成方面将具有更好的性能。在此，我们设计了反卷积工具“DeconPeaker”，它可以使用开放染色质数据集精确界定细胞亚群之间的独特性。使用该工具，我们同时评估了染色质可及性和基因表达数据集，以估计样品混合物中的细胞类型及其各自比例。与其他已知的反卷积方法相比，我们观察到预测值与“真实”比例之间的平均均方根误差最低（RMSE = 0.042），平均相关系数最高（ = 0.919）。作为概念验证，我们还测试了急性髓系白血病（AML）的染色质可及性数据，并成功获得了与AML进展相关的独特细胞类型。此外，我们表明，在识别细胞类型方面，染色质可及性比基因表达代表更基本的特征。综上所述，DeconPeaker作为一个强大的工具，有潜力结合不同的数据集（主要是染色质可及性和基因表达）并界定混合物中的不同细胞类型。DeconPeaker的Python包现已在https://github.com/lihuamei/DeconPeaker上提供。
