Hassaan Maan

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.

A generative transformer pretrained on over 33 million cells can be fine-tuned for cell-type annotation, batch and multi-omic integration, perturbation prediction and gene network inference, claiming superior performance.

DeepVelo estimates cell-specific transcriptional kinetics in scRNA-seq data using GCNs, enabling analysis of complex differentiation processes with time-varying rates.

scFormer uses transformers to learn cell and gene embeddings for single-cell data, achieving state-of-the-art results on multiple tasks.

This study evaluates how dataset imbalance affects single-cell RNA sequencing integration and introduces metrics to assess integration stability in imbalanced settings.