Asier Antoranz

This study maps glioblastoma tumor ecosystems over time using spatial single-cell profiling to identify prognostic subgroups and treatment responses.

CLEAR-AF improves autofluorescence subtraction in multiplexed tissue imaging by using polar transformation and Gaussian Mixture Models, enhancing specificity, sensitivity, and reproducibility.

CONCLAVE improves cell annotation in cyclic multiplexed immunofluorescence data by using consensus clustering and validation, enhancing accuracy and reproducibility.

COLLAGE improves image alignment for multiplexed immunohistochemistry by breaking alignment into smaller puzzles, enabling parallel processing and reducing errors.

QUAL-IF-AI is a deep learning tool that detects and manages artifacts in multiplex immunofluorescence images, achieving high accuracy and IoU across common artifact types.

The paper identifies NK cells as contributors to resistance to anti-PD1 therapy in immune-excluded melanomas using single-cell RNA sequencing and spatial proteomics.

ELITE deconvolves bulk transcriptomic data using linear programming to estimate cell type composition, outperforming existing methods in cases with similar expression profiles.

The paper introduces an in-silico method to identify patient-specific essential genes using constraint-based modeling and validates it against experimental data from cancer cell lines.