Department of Medicine, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.
Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA 94305-5101, USA.
Bioinformatics. 2021 Nov 18;37(22):4164-4171. doi: 10.1093/bioinformatics/btab409.
For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations.
We introduce 'CyAnno', a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of 'gated' cell types and the 'ungated' cells. By applying this framework on several CyTOF datasets, we demonstrated that including the 'ungated' cells can lead to a significant increase in the precision of the 'gated' cell types prediction. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.
The CyAnno is available as a python script with a user-manual and sample dataset at https://github.com/abbioinfo/CyAnno.
Supplementary data are available at Bioinformatics online.
为了在单细胞分辨率的 CyTOF 中进行大规模研究中的免疫系统监测，（半）自动化计算方法被应用于注释混合细胞类型的活细胞。在这里，我们表明，可以高度富集未定义的异质细胞，即“未门控”细胞，而当前的半自动方法忽略了它们的建模，导致注释错误分类。
我们引入了“CyAnno”，这是一种新颖的半自动方法，用于基于机器学习框架对未标记的细胞仪数据集进行去卷积，该框架利用手动门控训练数据进行集成建模，允许对“门控”细胞类型和“未门控”细胞进行建模。通过在几个 CyTOF 数据集上应用此框架，我们证明包括“未门控”细胞可以显著提高“门控”细胞类型预测的精度。CyAnno 可以比当前最先进的半自动方法更有效地识别单个细胞类型，包括稀有细胞。
CyAnno 可作为带有用户手册和示例数据集的 Python 脚本在 https://github.com/abbioinfo/CyAnno 上获得。
补充数据可在 Bioinformatics 在线获得。
