ArticleSmall science2024
Accurate and Flexible Single Cell to Spatial Transcriptome Mapping with Celloc.
Article in Small science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.Briefings in bioinformatics · 2026Article
- Transfer learning of multicellular organization via single-cell and spatial transcriptomics.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate mapping between single-cell RNA sequencing (scRNA-seq) and low-resolution spatial transcriptomics (ST) data compensates for both limited resolution of ST data and missing spatial information of scRNA-seq. Celloc, a method developed for this purpose, incorporates a graph attention autoencoder and comprehensive loss functions to facilitate flexible single cell-to-spot mapping. This enables either the dissection of cell composition within each spot or the assignment of spatial locations for every cell in scRNA-seq data. Celloc's performance is benchmarked on simulated ST data, demonstrating superior accuracy and robustness compared to state-of-the-art methods. Evaluations on real datasets suggest that Celloc can reconstruct cellular spatial structures with various cell types across different tissues and histological regions.
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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.