DiSTILL: A Hybrid Cloud-HPC Workflow System for Reproducible Spatial Transcriptomics Analysis

Myles Joshua Toledo Tan; Vasco Gerardo Hinostroza Fuentes; Nikhil Yerra; Maria Kapetanaki; Parisa Rashidi; Kejun Huang; Panayiotis V. Benos

arXiv · 2606.30693

DiSTILL: A Hybrid Cloud-HPC Workflow System for Reproducible Spatial Transcriptomics Analysis

Myles Joshua Toledo Tan·Vasco Gerardo Hinostroza Fuentes·Nikhil Yerra·Maria Kapetanaki·Parisa Rashidi·Kejun Huang·Panayiotis V. Benos

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Abstract

Spatial transcriptomics workflows increasingly combine large annotated data objects, notebook-based analyses, and resource-intensive statistical models that must be executed on high-performance computing (HPC) systems. In practice, these workflows are often difficult to reproduce because configuration, validation, stage execution, and artifact handling are fragmented across $\textit{ad hoc}$ scripts and manually edited notebooks. We present $\textit{DiSTILL}$ (Disease Diagnosis from Spatial Transcriptomics via Interpretable Latent Learning), a hybrid cloud$-$HPC workflow system for reproducible spatial transcriptomics (ST) analysis. DiSTILL combines an application programming interface (API) backend built with $\texttt{FastAPI}$, a web frontend, a dataset and preset registry, and a Python pipeline generator that materializes run-specific execution bundles and $\texttt{SLURM}$ submission scripts. The system supports local, Secure Shell (SSH)-mediated, and pull-based poller execution modes, enabling HPC submission in environments where persistent API-initiated automation is restricted. We describe the system through the lens of an inflammatory bowel disease (IBD) ST workflow that operationalizes the analytical pipeline of Tan $\textit{et al.}$ into an auditable application layer. Accordingly, the contribution of this paper is a workflow systems contribution centered on reproducible execution, queue-based orchestration, configuration semantics, and deployment across a split cloud$-$HPC architecture. The broader application goal of DiSTILL is to support user-supplied datasets that satisfy the schema assumptions of the wrapped analytical pipeline.

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Myles Joshua Toledo Tan, Vasco Gerardo Hinostroza Fuentes, Nikhil Yerra, Maria Kapetanaki, Parisa Rashidi, Kejun Huang, Panayiotis V. Benos. 2026-06-28. DiSTILL: A Hybrid Cloud-HPC Workflow System for Reproducible Spatial Transcriptomics Analysis. https://arxiv.org/abs/2606.30693

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