Cytobank has released version 5.5.0 with enhancements to our API, enabling more flexible and functional workflows that leverage Cytobank’s secure infrastructure and cloud-based compute and storage. Among the enhancements are new API endpoints for viSNE, CITRUS, SPADE, sharing, Sample Tags, and compensation.
At Cytobank, we’ve seen emerging needs among scientists and research organizations the world over that are driving the development of our API. These needs often demand functionality beyond that given by basic browser-based analysis sessions, with themes including connecting the Cytobank platform directly to other information systems, allowing batch processing and chaining of native functionality, and supporting pull and push of data, configurations, statistics, and attachments from Cytobank to support external pipelines, algorithms, and studies.
In this article, we present a variety of workflows highlighting how the Cytobank API can increase the efficiency and velocity of research efforts. Illustrated workflows include:
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 thatcan help predict these outcomes ahead of time?
The Challenge: 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 »