This is Part I of a blog series where Cytobank’s Director of Informatics, Katherine Drake, PhD, will discuss important aspects of using CITRUS to your full advantage. She’ll help you understand when you should use CITRUS, what makes it so powerful, and what you need to know to choose the best setup options for your data.
Since we implemented CITRUS in Cytobank last fall, many scientists have taken advantage of its automated biomarker discovery pipeline to answer questions and find predictive models across a wide range of diseases. Here, I’ll discuss its capabilities and review some examples to help you imagine how you can use CITRUS, too. More »
Automatically Identify Predictive Biomarkers with Our Newest Algorithm
Why do some cancer patients respond well to immunotherapy but others do not?
Why do some people have a slow and painful recovery from surgery but others have a speedy recovery?
What are the biomarkers that can help predict these outcomes ahead of time?
High-dimensional single-cell analysis approaches are excellent for investigating such questions because many mechanisms of disease may only be visible at the single-cell level, eluding bulk analysis techniques. Emerging technologies such as high parameter fluorescence and mass cytometry, and powerful data analysis platforms like Cytobank, are providing unprecedented resolution for measuring single-cell biology. With these new technologies, a variety of specific cellular populations can be simultaneously identified, and anomalies can define different clinically relevant cohorts and serve as predictive diagnostics and prognostics.
The challenge of going from high-dimensional data to these useful findings lies in the analysis, which is often cumbersome, manual, subjective, and irreproducible. Our new version of the CITRUS algorithm aims to change that. More »