ArticleBriefings in bioinformatics2021
TIPS: trajectory inference of pathway significance through pseudotime comparison for functional assessment of single-cell RNAseq data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Allospecific splenic Tr1 cells drive effector T cell exhaustion through up-regulated Areg-EGFR signaling to promote transplant tolerance.Science advances · 2026Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- B cell receptor repertoire analysis in primary Sjogren's syndrome salivary glands identifies repertoire features associated with clinical activity.Arthritis research & therapy · 2024Article
- Screening single-cell trajectories via continuity assessments for cell transition potential.Briefings in bioinformatics · 2023Article
- Insights Gained and Future Outlook From scRNAseq Studies in Autoimmune Rheumatic Diseases.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.