ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions
ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions
复制标题
ClusterMap：比较不同实验条件下的多个单细胞 RNA-Seq 数据集
DOI：
10.1093/bioinformatics/btz024
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发表时间：
2019-09-01
期刊：
影响因子：
5.8
通讯作者：
Li, Hua
中科院分区：
文献类型：
--
作者：
Gao, Xin;Hu, Deqing;Li, Hua
动机
单细胞RNA-Seq有助于表征细胞类型异质性和发育过程。在不同条件下对单细胞谱的进一步研究使得能够在亚群水平上理解生物学过程和潜在机制。然而，开发适当的方法来比较多个scRNA-Seq数据集仍然具有挑战性。
结果
我们已经开发了一种系统的方法和工作流程，以促进不同生物背景下scRNA-seq图谱的比较。通过对每个亚组的标记基因进行分层聚类，QuantiterMap匹配不同样本中的细胞亚型，并提供“相似性”作为量化匹配质量的指标。我们介绍了一个纯度树切割方法专门为这个匹配问题。我们使用Circos图和重组方法来简洁地可视化结果。此外，我们提出了一个新的度量“可分性”来总结所有样本对之间的子总体变化。在这些案例研究中，我们证明了QuestionterMap能够为我们提供对不同条件下细胞亚群的不同分子机制的进一步了解。
可用性
QuestterMap是用R实现的，可以在https://github.com/xgaoo/ClusterMap上找到。
补充资料
补充数据可在Bioinformatics在线获得。
Motivation
Single cell RNA-Seq facilitates the characterization of cell type heterogeneity and developmental processes. Further study of single cell profiles across different conditions enables the understanding of biological processes and underlying mechanisms at the sub-population level. However, developing proper methodology to compare multiple scRNA-Seq datasets remains challenging.
Results
We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA-seq profiles across distinct biological contexts. Using hierarchical clustering of the marker genes of each sub-group, ClusterMap matches the sub-types of cells across different samples and provides "similarity" as a metric to quantify the quality of the match. We introduce a purity tree cut method designed specifically for this matching problem. We use Circos plot and regrouping method to visualize the results concisely. Furthermore, we propose a new metric "separability" to summarize sub-population changes among all sample pairs. In the case studies, we demonstrate that ClusterMap has the ability to provide us further insight into the different molecular mechanisms of cellular sub-populations across different conditions.
Availability
ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap.
Supplementary information
Supplementary data are available at Bioinformatics online.