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

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

q-bio.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

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

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN↗

arXiv2026

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN↗

arXiv2026

Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation

Single-cell and spatial foundation models promise transferable biological representations, yet their generality remains largely untested across modalities, biological domains and analytical tasks. We benchmarked six representative models, Nicheformer, CellPLM, scGPT-spatial, GenePT, scELMo and Novae, using a harmonised framework spanning scRNA-seq, spatial transcriptomics and Perturb-seq. We evaluated zero-shot and continually pretrained clustering, supervised annotation, marker-gene concordance and perturbation prediction. Model performance was strongly conditional: expression-trained cell-level transformers best resolved many cell-identity tasks, spatial and graph-aware models better preserved tissue architecture, and language-derived gene embeddings were competitive for selected perturbation-response metrics. No model dominated across tasks, and rankings shifted with modality, preprocessing, tokenisation, biological prior, domain shift and metric choice. This benchmark provides practical guidance for model selection and argues that future models should be judged by biological generalisation, interpretability and perturbation-grounded validity, not by scale or leaderboard performance alone.

q-bio.GN↗