Alina Selega

This study uses multi-modal VAEs to integrate multiple views of highly multiplexed imaging data, improving the prediction of clinical outcomes and identification of cell states.

The paper introduces ISOMERIC, a deep learning framework to separate copy number-dependent and independent variation in single-cell transcriptomes, showing distinct associations with clinical subtypes and tumour microenvironment phenotypes.

This paper introduces SpatialDIVA, a method to disentangle spatial transcriptomics and histopathology imaging data, showing it outperforms baselines in variation separation and batch effect removal.

STARLING quantifies cell populations from spatial protein expression data while accounting for segmentation errors, identifying known cell types and quantifying heterogeneity in human tonsil tissue.

The paper applies 288 scRNA-seq pipelines to 86 datasets and builds machine learning models to predict pipeline performance based on dataset and pipeline characteristics.

The paper introduces a multi-objective Bayesian optimization method that adaptively updates scalarization functions for biomedical data analysis workflows, demonstrating it on single-cell RNA sequencing and imaging data.