Pouyan Maziyar Baran, Nourani Mehrdad
IEEE J Biomed Health Inform. 2017 Jul;21(4):1172-1181. doi: 10.1109/JBHI.2016.2565561. Epub 2016 May 10.
Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.
诸如大脑和骨髓等复杂组织是由多种细胞类型组成的。随着生物组织结构研究的进展，细胞类型特异性研究的作用变得越来越重要。诸如单细胞流式细胞术等新型测序技术为研究人员提供了获取有价值生物数据的途径。将机器学习技术应用于这些高通量数据集，能深入洞察这些细胞所属组织的细胞图谱。在本文中，我们提出使用基于随机森林的单细胞分析方法，这是一种新的基于机器学习的技术，用于利用单细胞流式细胞术数据对复杂组织的不同细胞类型进行分析。我们的技术利用随机森林来捕捉细胞标志物依赖性，并使用细胞网络概念对细胞群体进行建模。这个细胞网络有助于我们发现组织中存在哪些细胞类型。我们在公共领域数据集上的实验结果表明，我们的技术在提取复杂组织的细胞群体方面具有良好的性能和准确性。
