Céline Vallot

This study identifies the pentose phosphate pathway's role in regulating myeloid versus lymphoid lineage specification in hematopoietic stem and progenitor cells through metabolic state-fate mapping.

This paper develops a workflow for single-nucleus transcriptomics from FFPE tumor samples, showing reproducible cellular composition analysis across six cancer types.

This study uses single-cell RNA sequencing to analyze metastatic urothelial carcinoma and identifies macrophage-driven resistance to PD-(L)1 blockade as a key mechanism.

The paper introduces OneCell CUT&Tag, a single-cell multi-omics method that captures chromatin, transcriptome, and surface marker data, revealing epigenomic reprogramming during mammary gland basal-to-luminal transition.

This paper develops a single-nucleus multimodal framework to analyze transcriptional and histone modification landscapes in ovarian cancer, revealing epigenomic priming of chemoresistant states and its impact on treatment outcomes.

The paper identifies a shared persistence program in triple-negative breast cancer across treatments and patients, revealing key drivers and mechanisms of drug resistance.

MAYA quantifies pathway activation modes across cell populations, improving granularity and robustness in single-cell data analysis.

The paper benchmarks scHPTM analysis pipelines to identify best practices for experimental design and data analysis.

This review surveys recent machine learning approaches for analyzing single-cell omics data from 2019-2020, focusing on methods for dimensionality reduction, batch normalization, cell type classification, trajectory inference, and multimodal integration.

ChromSCape is an R/Shiny tool for analyzing single-cell chromatin data, enabling subpopulation identification and differential region analysis in epigenomic studies.

This study benchmarks dimensionality reduction methods for scRNA-seq by varying parameters and finds that some methods benefit from tuning while others perform well with defaults.