ReviewBriefings in bioinformatics2025
An algorithmic perspective on deciphering cell-cell interactions with spatial omics data.
Review in Briefings in bioinformatics, 2025. 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
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
2 citing papers in PubMed.
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Immune niches in brain tumors: glial-immune cell interactions and spatial microdomains.Acta neuropathologica communications · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The advent of technologies to measure molecule information from a tissue that retains spatial information paved the way for the development of cell-cell interaction (CCI) methods. Even though these spatial technologies are still in their relative infancy, the developed methods promise more accurate analysis of CCIs due to the inclusion of spatial data. In this review, we outline these methods and provide a high-level view of the algorithms they employ. Moreover, we investigate how they deal with the spatial nature of the data they use and what types of downstream analyses they execute. We show that spatial CCI methods can broadly be classified into supervised learning, statistical correlation, and optimization methods that are used for either refinement of CCI networks, spatial clustering, differential expression analysis, or analysis of signal propagation through a tissue. In the end, we highlight some avenues for the development of complementary CCI methods that exploit advances in spatial data or alleviate certain downsides of the current methods.
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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.