Matrix spillover remains a significant issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, deep neural networks have emerged as novel tools to mitigate matrix spillover effects. AI-mediated approaches leverage complex algorithms to detect spillover events and compensate for their consequences on data … Read More