st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics

Clicks: 53
ID: 326673
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #136 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Multi-timepoint spatial transcriptomics enables study of developmental processes in native tissue context, but cell-state mixtures within spots and lack of direct spatial correspondence across sections complicate trajectory inference and biological interpretation. RESULTS: st2traj is a deconvolution-informed trajectory framework using spot-state composition for multi-timepoint spatial trajectory inference. In a human heart pseudo-spot benchmark, DECODE showed competitive and balanced performance among five deconvolution methods. In multi-timepoint human heart data, unscaled DECODE-derived proportions produced smoother trajectory fields and stronger agreement with expression-derived marker programs than normalized spot-level expression. st2traj also showed greater spatial coherence than spaTrack, while exploratory comparisons with moscot and CASCAT revealed complementary method-specific strengths. Application to an independent chicken heart dataset recovered stage-associated trajectory changes across D7, D10, and D14. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/xiaoxiaoxier/st2traj. Software v0.1.0 and processed data are archived at Zenodo: https://doi.org/10.5281/zenodo.21487030 and https://doi.org/10.5281/zenodo.21502094. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7204439907 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhuo Wang, Chiping Zhang
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag645
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag645/70818946/btag645.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.