CyLinter is a specialized computational tool developed to systematically identify and exclude cells compromised by visual distortions and image-processing artifacts in high-dimensional multiplex bioimaging data. By addressing these common yet often overlooked sources of error, CyLinter empowers researchers to preserve the integrity of tissue-derived single-cell datasets. Through the automation of quality control and data validation processes, it not only streamlines workflows but also significantly improves the accuracy and reproducibility of spatial biomarker analyses. This capability is especially critical in fields such as cancer research and immunology, where the precise mapping of cellular neighborhoods and microenvironmental contexts is fundamental to understanding complex disease mechanisms. With its robust design and scalability, CyLinter serves as both a foundational tool for discovery and a catalyst for advancing translational insights.
CyLinter is actively evolving to meet the growing demands of the scientific community. Ongoing development focuses on expanding compatibility with emerging bioimaging platforms, integrating advanced machine learning algorithms for even more precise artifact detection, and continually incorporating automated quality control (QC) tools into the pipeline. We have an active research project dedicated to enhancing these automated QC features, which will further improve data integrity and analysis efficiency. By building a community around CyLinter, we aim to accelerate innovation, promote transparency, and empower researchers worldwide to unlock deeper insights from their spatial biology data.
For more details on our current research projects, visit our jobs page.