ArticleNature communications2023
Predictive and robust gene selection for spatial transcriptomics.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Article
- Conserved and specialized features of thalamocortical wiring revealed by single-cell projection mapping in mouse and marmoset.bioRxiv : the preprint server for biology · 2026Article
- Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.Nature biotechnology · 2026Article
- Optimal gene panel selection for targeted spatial transcriptomics experiments.Nucleic acids research · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Article
- 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
- Article
- STAHD: a scalable and accurate method to detect spatial domains in high-resolution spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Imputation integrates single-cell and spatial gene expression data to resolve transcriptional networks in barley shoot meristem development.Nature plants · 2026Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- Spatially Resolved Profiling of Compartmentalized Muscle and Brain Inflammation.European journal of immunology · 2025Review
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Optimal Gene Panel Selection for Targeted Spatial Transcriptomics Experiments.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
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- Reprogramming the tumor microenvironment: synergistic mechanisms of antibody-drug conjugates and immune checkpoint inhibitors.Antibody therapeutics · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.
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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.