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.
CITRUS is a black box algorithm that takes your scientific question and returns a set of cell-type specific biomarkers to help you answer that question. Importantly, it does this without relying on prior knowledge or subjective gating to define cell types, and the biomarkers are identified in a statistically sound manner so that you can be more confident that you’re following up on true positive results.
CITRUS is set up to answer two types of questions. You can ask either ‘How are my groups different?’ or ‘How can I best predict the differences between my groups?’
The first step in thinking about how you can take advantage of CITRUS is to define the groups you want to compare. You can compare any groups where you hypothesize that the difference between them is driven by differences in the abundance of various cell types, the activation or inhibition of signaling in these cell types, or the presence or absence of markers in these cell types. CITRUS will tell you which of these “features” are significantly different between the groups you define.
Important Examples from Published Articles
The seminal example of using CITRUS for single-cell biomarker discovery was published in Science Translational Medicine in 2014. In this example, Gaudillière, Fragiadakis et al. used CITRUS to find signaling patterns among specific subsets of CD14+ monocytes that expand after hip surgery and are associated with time to improvement in various recovery-related outcomes. You can watch Dr. Fragiadakis discuss how they used CITRUS to make these discoveries and hear CITRUS’s creator, Dr. Robert Bruggner, answer questions about the algorithm in our recorded webinar.
In another powerful application of CITRUS, Ben-Shaanan et al. (Nature Medicine, 2016) demonstrated that the brain’s reward system is causally related to immune response using a mouse model of ventral tegmental area activation. The authors used CITRUS to identify specific changes in functional marker expression in immune cells in the spleen and blood of these mice after their reward system had been activated, and then went on to demonstrate that this reward system activation increases both the primary immune response to bacterial exposure, and the immune response upon re-exposure. This work has interesting implications for understanding the biology behind placebo response.
Lau et al. published another compelling CITRUS example in Pediatric Transplantation (2016), where they found a sub-population of CD4+ T cells whose increased abundance predicted increased tolerance to liver transplant (without immunosuppression) in children. They demonstrated that this new sub-population is distinct from classic Tregs, which is important because Treg abundance has a low specificity for predicting tolerance to liver transplant in children.
These examples illustrate a few of the many ways in which you might use CITRUS to identify cell-type specific biomarkers that distinguish groups you’re interested in. What can you use it for in your research?
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- Getting the Most out of CITRUS Part II: What Cross-Validation & False Discovery Rate Do For You
- Getting the Most out of CITRUS Part III: Choose the Best Parameters for Your Data
- Getting the Most out of CITRUS Part IV: Testing Multiple Endpoints at Once