ArticleiScience2026
Density-adjusted analysis of cell interactions to decipher tissue landscape changes.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-cell proximity influences tissue homeostasis and disease progression, yet robust quantification across varying cell abundances remains challenging. We introduce a Monte Carlo simulation framework using the G-function as a spatial randomness reference to detect proximity differences between case groups independent of cell count. Three metrics, G-area, G-difference, and G-ratio, were evaluated for summarizing G-function outputs, alongside established approaches such as the Morisita-Horn Index and likelihood ratio. G-area most accurately captured group-level proximity changes. To demonstrate generalizability, we validated G-area in two external multiplex imaging datasets from colorectal and prostate cancer. This framework provides a cell-count-robust method for spatial analysis, enabling more reliable detection of microenvironmental changes across diseases and imaging platforms.
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.