Large-scale transcriptomic data is used by biologists for the discovery of new molecular patterns or cell subpopulations. Clustering is one of the most popular methods for dimensionality reduction and data analysis for large scale datasets. The major problem while clustering the data is the selection of the optimal number of clusters (k) for each dataset and to discover new insights from it. We have developed Recursive Consensus Clustering (RCC), an unsupervised clustering algorithm for novel subtype discovery from both bulk and single-cell datasets. RCC is available as an R package and facilitates the generation of new biological insights through intuitive visualization of clustering results.

生物学家使用大规模的转录组数据来发现新的分子模式或细胞亚群。聚类是用于大规模数据集的降维和数据分析的最受欢迎的方法之一。聚类数据时的主要问题是为每个数据集选择最佳聚类数（k），并从中发现新的见解。我们已经开发了递归共识聚类（RCC），这是一种用于从大量和单细胞数据集中发现新型亚型的无监督聚类算法。RCC作为R软件包提供，并且通过对聚类结果的直观可视化，促进了新的生物学见解的产生。