University of the South Pacific and a Lecturer at Fiji National University.
Combinatics Inc., Tokyo, Japan.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa306.
Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing (RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency.
We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data.
The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/.
Supplementary data are available online at https://academic.oup.com/bib.
下一代测序技术的进步使得在单细胞分辨率下进行转录组学研究并快速生成大量单细胞 RNA 测序（RNA-seq）数据成为可能。因此，分析这些数据的工具也需要不断发展，以提高准确性和效率。
我们提出了 FEATS，这是一个用于分析单细胞 RNA-seq 数据的 Python 软件包。FEATS 能够执行多种任务，如估计聚类数、进行异常值检测和整合来自不同实验的数据。我们开发了一种基于单变量特征选择的聚类方法，该方法涉及选择信息量最大的特征来提高聚类性能。这是因为细胞类型通常是通过仅使用少数几个已知标记基因的表达来手动确定的。在各种单细胞 RNA-seq 数据集上，FEATS 在调整后的 Rand 指数和估计聚类数方面的性能优于当前工具。与其他工具相比，FEATS 在聚类方面的性能提高了 22%，并且更准确地估计了聚类数。除了聚类估计外，FEATS 还可以进行异常值检测和数据集成，同时提供出色的计算性能。因此，FEATS 是一种功能全面的聚类工具，能够解决单细胞 RNA-seq 数据聚类过程中的挑战。
FEATS 的安装说明和文档可在 https://edwinv87.github.io/feats/ 上获得。
补充数据可在 https://academic.oup.com/bib/ 上在线获得。
