Khalil Ouardini

Comprehensive benchmarking of batch integration methods for spatial transcriptomics using a large-scale cancer atlas

This study benchmarks batch integration methods for spatial transcriptomics using a cancer atlas, finding probabilistic methods excel in batch correction while graph-based methods perform better in spatial conservation.

Joint probabilistic modeling of pseudobulk and single-cell transcriptomics enables accurate estimation of cell type composition

The paper introduces MixupVI, a deep generative model for reference-free deconvolution of bulk RNA-seq data using single-cell transcriptomics, and releases PyDeconv for benchmarking deconvolution algorithms.

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

This study evaluates deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data, finding that non-DL methods perform comparably on survival tasks while DL methods excel in gene essentiality prediction.