A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task.

Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System–Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes.