TIPS: Trajectory Inference of Pathway Significance through Pseudotime Comparison for Functional Assessment of single-cell RNAseq Data

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 transcription factor targets to enable further mechanistic insights into a biological process. TIPS returns both the key pathways whose changes are associated with the 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 and/or large cohort RNAseq data.

Trajectory mapping of primary Sjögren's syndrome via transcriptome learning demonstrates limitations of peripheral blood sequencing

“…In order to identify the pathways contributing to the progression of pSS transcriptomes, we used the TIPS workflow 21 in R with recommended settings. Using the Reactome pathway knowledgebase 22 as our reference, we assessed the relative contribution of all pathways with at least 20 genes detected in each dataset through a process of iterated trajectory mapping.…”