ArticleBioinformatics (Oxford, England)2026
Inferring gene expression profiles of tumor clones.
Article in Bioinformatics (Oxford, England), 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
3 authors.
Funding
Abstract
motivationTumors are heterogeneous mixtures of clonal subpopulations of cells that co-evolve in time and space. Apart from genetic differences, cancer clones may reside in different locations in the tumor tissue and show various transcriptional phenotypes, with specific gene expression profiles and marker genes. Computational tools that simultaneously infer genotypes and transcriptional phenotypes of clones in tumor tissues are lacking.
resultsWe introduce ClonalGE, a probabilistic graphical model identifying cancer clone genotypes, their gene expression profiles, and spatial mapping within tumor tissue. The model proceeds by probabilistic matching of single nucleotide variant sites in spatial transcriptomics to clone-specific variants inferred from DNA sequencing data, and by deconvoluting gene expression in spatial transcriptomics spots into contributions from individual clones. We propose a procedure that utilizes the Monte Carlo posterior samples for identification of differentially expressed genes between clones, using a Highest Density Interval plus Region of Practical Equivalence (HDI+ROPE) criterion that jointly requires credible and practically meaningful effect sizes. ClonalGE demonstrates superior performance on simulated data. Applied to a prostate cancer sample, it reveals clone-specific expression patterns and outperforms its predecessor model in accurately inferring gene expression profiles and explaining spatial transcriptomics signals. Our approach advances the functional and phenotypic characterization of clonal cell subpopulations in tumors. AVAILABILITY: The implementation of ClonalGE and the data used in this study can be accessed in the following GitHub repository https://github.com/szczurek-lab/ClonalGE/. The benchmarking data alongside the intermediate results are available on Zenodo at DOI: 10.5281/zenodo.20446991.
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.