Automated and reproducible cell identification in mass cytometry using neural networks.

ABSTRACT: The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometr

SUBMITTER: Saihi H 

PROVIDER: S-EPMC10630086 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature