ArticleComplex networks & their applications. International Conference on Complex Networks and Their Applications2025
Cell signaling characterization for spatial transcriptomics (ST) data using network analysis.
Article in Complex networks & their applications. International Conference on Complex Networks and Their Applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation.Complex networks & their applications. International Conference on Complex Networks and Their Applications · 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
2 authors.
Funding
Abstract
We describe a network analysis-based cell-cell communication method for Spatial Transcriptomics (ST) data. For each evaluated ligand-receptor interaction, we define a fully connected, directed and weighted network model where nodes represent the individual ST locations with directed edge weights set to the product of the reduced rank reconstructed expression values for the ligand at the source location and cognate receptor at the target location divided by the squared distance between the locations. Using this network, we compute the weighted in-degree centrality to quantify signaling activity of the target ligand-receptor interaction at each location. Our method is validated for three interactions on a real ST dataset against five different cell-cell communication strategies. We report that our method captures the simultaneous expression heterogeneity in both the ligand and the receptor and generates biologically plausible cell communication profiles for the Wnt3-Fzd1, Ephb1-Efnb3 and Ptprc-Cd22 interactions. An important finding of this work is the importance of building network models for ST data using a low dimensional embedding of the gene-level data.
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.