Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis

作者：Pan, Lu; Mou, Tian; Huang, Yue; Hong, Weifeng^*; Yu, Min^*; Li, Xuexin^*

来源：Molecular Biology and Evolution, 2023, 40(12): msad267.

DOI：10.1093/molbev/msad267

• multiomics
• single-cell
• analysis workflow
• multimodal analysis

The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.

• 单位
  南方医科大学; 中国医科大学; 广东省人民医院; 复旦大学