Optimal gene panel selection for targeted spatial transcriptomics experiments.
The 2 matches
- [1] § Material and methods › Implementation details ↔ reconst/trainer.py, lines 7–52 · score 0.57 · weight decay, Adam, penalty, optimizer, loss, autoencoder
- [2] § Material and methods › Implementation details ↔ reconst/model.py, lines 7–38 · score 0.53 · LeakyReLU, dropout, decoder, encoder, model, weight
Paper
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The authors' code
Python · 85 lines · 2.6 KB · no license · 1 match
trainer.py at commit 7479571, no license · at the source
Overview
- Department of Statistics, University of Georgia, Athens, GA 30602, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
Abstract
Spatial transcriptomics analysis is a powerful approach for dissecting the structure of tissue microenvironment and uncovering the mechanism of cell–cell communications. However, existing technologies are limited by either spatial resolution or gene coverage. Most single-cell-resolution technologies target only a few hundred preselected genes, whose choice plays an important role in the overall analysis. It remains a challenge to optimally design a gene panel to maximize the utility of spatial transcriptomics profiling. To fill this gap, we introduce a novel method, named ReconST, to automatically design optimal gene panels for spatial transcriptomics profiling. ReconST leverages information from existing scRNA-seq data and identifies the optimal subset of genes by using a gated autoencoder. By using a high-coverage mouse brain MERFISH dataset and a fetal lung dataset as the reference benchmarks, we showed that ReconST outperforms existing methods in terms of reconstruction accuracy, spatial pattern preservation, and computing efficiency. As such, ReconST provides a useful and generally applicable tool for optimal gene panel design, which in turn can significantly enhance the utility of spatial transcriptomics profiling in a wide range of biomedical investigations.
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
Repositories
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haoranlustat.github.io/reconst
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
Zenodo 20434940
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
7 files
- example.ipynb — Jupyter, 169 lines
- reconst/
__init__.py — Python, 15 lines - reconst/
data.py — Python, 51 lines - reconst/
model.py — Python, 38 lines - reconst/
trainer.py — Python, 85 lines - setup.py — Python, 16 lines
- README.md — Text, 85 lines
haoranlustat/reconst
7479571b9e579a00ff31d7b703ac0cb74ad1494c, 3 December 2025Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
7 files, not copied: shown from their source
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- example.ipynb — Jupyter, 169 lines, shown from its source
- reconst/
__init__.py — Python, 15 lines, shown from its source - reconst/
data.py — Python, 51 lines, shown from its source - reconst/
model.py — Python, 38 lines, 1 match, shown from its source - reconst/
trainer.py — Python, 85 lines, 1 match, shown from its source - setup.py — Python, 16 lines, shown from its source
- README.md — Text, 85 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data availability
The software of the ReconST method is developed in a python package submitted as Supplementary file and is publicly available at https://
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 13 MeSH terms, 2 funders, 51 references.
Cite
This paper
Lu, H., Fang, L., Zeng, O., Zhong, W., Yuan, G.-C., & Ma, P. (2026). Optimal gene panel selection for targeted spatial transcriptomics experiments. Nucleic acids research, 54(11), gkag621. https://
BibTeX
@article{lu2026optimal,
author = {Lu, Haoran and Fang, Luyang and Zeng, Orlando and Zhong, Wenxuan and Yuan, Guo-Cheng and Ma, Ping},
title = {{Optimal gene panel selection for targeted spatial transcriptomics experiments}},
journal = {Nucleic acids research},
year = {2026},
month = jun,
volume = {54},
number = {11},
pages = {gkag621},
publisher = {Oxford University Press},
issn = {0305-1048},
doi = {10.1093/
url = {https://
pmid = {42306947},
pmcid = {PMC13273311}
}
RIS
TY - JOUR
AU - Lu, Haoran
AU - Fang, Luyang
AU - Zeng, Orlando
AU - Zhong, Wenxuan
AU - Yuan, Guo-Cheng
AU - Ma, Ping
TI - Optimal gene panel selection for targeted spatial transcriptomics experiments
T2 - Nucleic acids research
J2 - Nucleic Acids Res
PY - 2026
DA - 2026/
VL - 54
IS - 11
SP - gkag621
SN - 0305-1048
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
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