ArticleInnovation (Cambridge (Mass.))2026
stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.
Article in Innovation (Cambridge (Mass.)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Knowledge-graph-enhanced multi-scale modeling for drug-drug interaction prediction.Molecular therapy. Nucleic acids · 2026Article
- STGNET: extending panel coverage in imaging-based spatial transcriptomics using deep generative adversarial networks.Briefings in bioinformatics · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics (ST) preserves spatial context in gene expression analysis yet faces limitations like low resolution and RNA capture inefficiency. To address these, we present stSCI, a computational method integrating single-cell (SC) and ST data into a unified, batch-corrected embedding space. stSCI employs a fusion module with three specialized optimization tasks to generate biologically preserved joint latent representations, enabling five key analyses: spatial domain identification in single/multi-slice ST data, ST deconvolution predicting cell type proportions in low-resolution spots, SC spatial coordinate reconstruction using ST references, and crossmodality batch correction. Evaluated on 13 different ST datasets spanning sequencing- and imaging-based platforms, and benchmarked against 27 state-of-the-art methods, stSCI improves spatial domain identification, maps cell type proportions in ST data, accurately reconstructs tissue architecture and regional structures, and integrates SC/ST datasets by removing batch effects without compromising biological signals. In a key application, stSCI successfully resolves the dynamic spatiotemporal response of a lymphatic niche during
Indexed as
Identifiers
What OpenQuestion holds
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.