ArticleGenome biology2024
scGIST: gene panel design for spatial transcriptomics with prioritized gene sets.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- scGPD: single-cell informed gene panel design for targeted spatial transcriptomics.Briefings in bioinformatics · 2026Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- scGPD: single-cell informed gene panel design for targeted spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Comparison of spatial transcriptomics technologies using tumor cryosections.Genome biology · 2025Article
- Spatial-Omics Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.
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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.