TIPS: trajectory inference of pathway significance through pseudotime comparison for functional assessment of single-cell RNAseq data. Issue 5 (29th April 2021)

• Record Type: Journal Article
• Authors: Zheng, Zihan; Qiu, Xin; Wu, Haiyang; Chang, Ling; Tang, Xiangyu; Zou, Liyun; Li, Jingyi; Wu, Yuzhang; Zhou, Jianzhi; Jiang, Shan; Wan, Ying; Ni, Qingshan
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-04-29
• Subjects: trajectory mapping -- pseudotime -- TIPS -- pathway analysis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab124
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Recent advances in bioinformatics analyses have led to the development of novel tools enabling the capture and trajectory mapping of single-cell RNA sequencing (scRNAseq) data. However, there is a lack of methods to assess the contributions of biological pathways and transcription factors to an overall developmental trajectory mapped from scRNAseq data. In this manuscript, we present a simplified approach for trajectory inference of pathway significance (TIPS) that leverages existing knowledgebases of functional pathways and other gene lists to provide further mechanistic insights into a biological process. TIPS identifies key pathways which contribute to a process of interest, as well as the individual genes that best reflect these changes. TIPS also provides insight into the relative timing of pathway changes, as well as a suite of visualizations to enable simplified data interpretation of scRNAseq libraries generated using a wide range of techniques. The TIPS package can be run through either a web server or downloaded as a user-friendly GUI run in R, and may serve as a useful tool to help biologists perform deeper functional analyses and visualization of their single-cell data.