Serval: a modular framework for decoding imaging based spatial transcriptomics data.
5 affiliations
- 1Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
- 2Graduate Bioinformatics Training Program, University of British Columbia, Vancouver, BC V5T 4S6, Canada.
- 3Genome Science and Technology Graduate Program, Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- 4Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V5T 1Z7, Canada.
- 5Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Abstract
Motivation
Imaging-based spatial transcriptomics technologies have opened new avenues for studying cellular organization and gene expression within intact tissues. However, the accuracy of downstream analyses depends critically on the decoding step that reconstructs barcodes from fluorescence patterns and maps them to gene identities. Despite a growing number of decoding methods, systematic benchmarking has been limited.Results
Here, we introduce Serval, a modular framework for developing and benchmarking decoding methods across diverse spatial transcriptomics platforms. Serval separates key decoding stages into independently configurable modules, enabling flexible integration of alternative algorithms. Using this framework, we develop CoSiD (Cosine Similarity Decoder), a novel method that improves transcript recovery by optimizing cosine similarity to known barcodes. We evaluate CoSiD and baseline methods on synthetic and real MERFISH datasets, showing that CoSiD achieves higher transcript recovery and superior correlation with expression references compared to existing methods. Furthermore, we demonstrate that the Serval framework generalizes beyond MERFISH. By extending to the DART-FISH platform, we show that CoSiD improves transcript recovery, clustering stability and supports more direct annotation of complex biological structures such as the human primary motor cortex. These results establish that modular decoding frameworks facilitate robust, platform-agnostic benchmarking, ultimately supporting more accurate spatial transcriptomics analysis across diverse biological samples.Availability
Serval and CoSiD are open source and publicly available at https://github.com/Roth-Lab/serval. Code for the simulation and analysis pipelines is available from the repositories described in the Data Availability section.
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BibTeX
@article{tsui2026serval,
title = {{Serval: a modular framework for decoding imaging based spatial transcriptomics data.}},
author = {Jenkin Tsui and Naila Adam and Woongcheol Choi and Luna Y Liu and Cristina Flores and Shadi Ansari and Esther Kong and Yukta Thapliyal and Hakwoo Lee and Shahid Haider and Isaac Von Riedemann and Ciara O'Flanagan and IMAXT Cancer Grand Challenges Consortium and Samuel Aparicio and Andrew Roth},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag707},
url = {https://europepmc.org/article/MED/42841740},
note = {CC BY},
}
RIS
TY - JOUR
TI - Serval: a modular framework for decoding imaging based spatial transcriptomics data.
AU - Jenkin Tsui
AU - Naila Adam
AU - Woongcheol Choi
AU - Luna Y Liu
AU - Cristina Flores
AU - Shadi Ansari
AU - Esther Kong
AU - Yukta Thapliyal
AU - Hakwoo Lee
AU - Shahid Haider
AU - Isaac Von Riedemann
AU - Ciara O'Flanagan
AU - IMAXT Cancer Grand Challenges Consortium
AU - Samuel Aparicio
AU - Andrew Roth
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag707
UR - https://europepmc.org/article/MED/42841740
AB - Motivation Imaging-based spatial transcriptomics technologies have opened new avenues for studying cellular organization and gene expression within intact tissues. However, the accuracy of downstream analyses depends critically on the decoding step that reconstructs barcodes from fluorescence patterns and maps them to gene identities. Despite a growing number of decoding methods, systematic benchmarking has been limited.Results Here, we introduce Serval, a modular framework for developing and benchmarking decoding methods across diverse spatial transcriptomics platforms. Serval separates key decoding stages into independently configurable modules, enabling flexible integration of alternative algorithms. Using this framework, we develop CoSiD (Cosine Similarity Decoder), a novel method that improves transcript recovery by optimizing cosine similarity to known barcodes. We evaluate CoSiD and baseline methods on synthetic and real MERFISH datasets, showing that CoSiD achieves higher transcript recovery and superior correlation with expression references compared to existing methods. Furthermore, we demonstrate that the Serval framework generalizes beyond MERFISH. By extending to the DART-FISH platform, we show that CoSiD improves transcript recovery, clustering stability and supports more direct annotation of complex biological structures such as the human primary motor cortex. These results establish that modular decoding frameworks facilitate robust, platform-agnostic benchmarking, ultimately supporting more accurate spatial transcriptomics analysis across diverse biological samples.Availability Serval and CoSiD are open source and publicly available at https://github.com/Roth-Lab/serval. Code for the simulation and analysis pipelines is available from the repositories described in the Data Availability section.
SN - 1367-4803
SN - 1367-4811
ER -