Innes Brendan T, Bader Gary D
Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.
The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S3E1, Canada.
F1000Res. 2018 Sep 21;7. doi: 10.12688/f1000research.16198.2. eCollection 2018.
Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.
单细胞RNA测序(scRNAseq)代表了一种新型显微镜,它能够在单个实验中测量来自复杂细胞混合物(如组织)的数千个单个细胞的转录组图谱。这项技术对于表征组织异质性特别有价值,因为它可用于识别和分类组织中的所有细胞类型。这通常是通过对数据进行聚类来完成的,其依据是特定类型的细胞共享相似的转录组,与组织中的其他细胞类型不同这一假设。然而,几乎所有的聚类算法都有可调参数,这些参数会影响它们在数据中识别的聚类数量。这里描述的R Shiny软件工具scClustViz提供了一个简单的交互式图形用户界面,用于探索scRNAseq数据并评估聚类结果的生物学相关性。鉴于细胞类型预期具有不同的基因表达模式,scClustViz使用聚类之间的差异基因表达作为一种度量,在多个聚类分辨率水平上评估聚类结果与数据的拟合度。这有助于选择一个聚类参数用于进一步分析。scClustViz还提供了以下交互式可视化:技术因素(如预测的细胞周期阶段和其他元数据)的聚类特异性分布;聚类-wise基因表达统计,以简化细胞类型注释和识别细胞类型特异性标记基因;以及所有细胞和细胞类型上的基因表达分布。scClustViz为scRNAseq实验中的细胞类型分类的可视化、评估和生物学解释提供了一个交互式界面,该界面可以轻松添加到现有的分析流程中,使生物信息学家能够进行定制,同时使生物学家无需计算专业知识就能探索他们的结果。它可在https://baderlab.github.io/scClustViz/获取。