Marie-Liesse Asselin-Labat

Binning transcript and cell coordinates into spatial vectors lets jazzPanda call smaller, spatially concordant marker sets than Wilcoxon or t-tests on Xenium/CosMx data, while modelling replicates and negative-control background.

The paper identifies CD14+CD4+ T cells in NSCLC tumor microenvironment driven by TNF, linked to poor patient survival.

This study uses functional imaging and lung mechanics to assess how tumor growth affects lung function in mice, finding significant changes in ventilation and mechanics post-tumor induction.

LongBench benchmarks long-read RNA-sequencing technologies using a cross-platform dataset of cancer cell lines, showing high gene-level concordance but reduced transcript-level consistency across platforms.

This study shows that varying levels of oncogenic KRAS drive different cell states and metastatic progression in lung adenocarcinoma, with implications for treatment.

This paper uses random forests to analyze spatial cell interactions in tumor microenvironments and shows their predictive power for patient outcomes.

This study shows that library size in spatial transcriptomics data confounds biological signals and recommends against normalizing it unless necessary.

The paper demonstrates that intron reads from RNA-seq are informative and introduces methods to analyze them for transcriptional changes and intron retention.

Glimma provides interactive graphics for gene expression analysis to enhance data exploration and sharing between bioinformaticians and biologists.