Publications

Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data

Heidari, E.*, Moorman, A.*, Unyi, D., et al.

bioRxiv 2025

Lead developer. I reframed cell segmentation as heterogeneous-graph link prediction between transcript and cell nodes and built the multi-GPU pipeline (10–100M nodes), about a thousand times faster than the tools before it. Under revision at Nature Methods.

Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

Breinig, M.*, Lomakin, A.*, Heidari, E.*, et al.

Nature Biomedical Engineering 2025

Co-lead developer. Built the spatial phenotyping pipeline linking genetic perturbations to tumour microenvironments.

SpatialData: an open and universal data framework for spatial omics

Marconato, L.*, Palla, G.*, Yamauchi, K. A.*, Virshup, I.*, Heidari, E., et al.

Nature Methods 22(1):58–62 2025

Contributor. Designed and built the flagship multi-layer breast-cancer analysis.

snRNA-seq stratifies multiple sclerosis patients into distinct white matter glial responses

Macnair, W., Calini, D., Agirre, E., Heidari, E., et al.

Neuron 113(3):396–410.e9 2025

Contributor. Built the large-scale sc/snRNA-seq pipelines behind the analysis, across more than two hundred patients and about a million cells: integration, cell-type annotation, and the downstream statistics.

Meta-analysis of single-cell method benchmarks reveals the need for extensibility and interoperability

Sonrel, A., Luetge, A., Soneson, C., Mallona, I., Germain, P. L., Heidari, E., et al.

Genome Biology 24(1):119 2023

Contributor. Analysis and synthesis of the benchmarking results, and the software-design argument that came out of them.

Supervised spatial inference of dissociated single-cell data with SageNet

Heidari, E., Lohoff, T., Tyser, R. C. V., Marioni, J. C., Robinson, M. D., Ghazanfar, S.

bioRxiv 2022

Lead developer. Graph-attention spatial inference over a learned gene-interaction network. My master’s thesis; it won the ETH Medal.

An end-to-end workflow for high-throughput discovery of clinically relevant insights from large biomedical datasets

Heidari, E., Sadeghi, M. A., Meresht, V. B., et al.

bioRxiv 2020

Lead developer. A modular analytics pipeline for population-scale biomedical data, built for reproducibility and automation.

Pin1 regulatory miRNAs as novel candidates for Alzheimer's disease treatment

Heidari, E., Siavashani, E. S., Rasooli, M., et al.

bioRxiv 2018

Lead analyst. Statistical and meta-analysis identifying candidate regulatory miRNAs.