Livne Dani, Efroni Sol
The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
BioData Min. 2024 Dec 24;17(1):60. doi: 10.1186/s13040-024-00416-7.
Pathway analysis is a powerful approach for elucidating insights from gene expression data and associating such changes with cellular phenotypes. The overarching objective of pathway research is to identify critical molecular drivers within a cellular context and uncover novel signaling networks from groups of relevant biomolecules. In this work, we present PathSingle, a Python-based pathway analysis tool tailored for single-cell data analysis. PathSingle employs a unique graph-based algorithm to enable the classification of diverse cellular states, such as T cell subtypes. Designed to be open-source, extensible, and computationally efficient, PathSingle is available at https://github.com/zurkin1/PathSingle under the MIT license. This tool provides researchers with a versatile framework for uncovering biologically meaningful insights from high-dimensional single-cell transcriptomics data, facilitating a deeper understanding of cellular regulation and function.
通路分析是一种强大的方法，可用于从基因表达数据中阐明见解，并将此类变化与细胞表型相关联。通路研究的总体目标是在细胞环境中识别关键分子驱动因素，并从相关生物分子组中发现新的信号网络。在这项工作中，我们展示了PathSingle，这是一种基于Python的通路分析工具，专为单细胞数据分析量身定制。PathSingle采用独特的基于图的算法，能够对多种细胞状态进行分类，例如T细胞亚型。PathSingle设计为开源、可扩展且计算高效，可在https://github.com/zurkin1/PathSingle上根据MIT许可获得。该工具为研究人员提供了一个通用框架，用于从高维单细胞转录组学数据中发现具有生物学意义的见解，有助于更深入地理解细胞调节和功能。
