SPADE is now available on hosted models of Cytobank and on Fluidigm Cytobank. To help you get started, we’ve added a number of Knowledge Base articles and a demo movie. We’ll be adding more tutorials and resources shortly, so stay tuned. You can subscribe to our newsletter for the latest updates.
Contact info@cytobank.org to get started.
– Angela
What is SPADE?
SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a way to automatically identify populations in multidimensional flow cytometry data files. SPADE clusters cells into populations and then projects them into a tree like the one shown below. SPADE works for data from both ‘classic’ fluorescence flow cytometry and mass cytometry.
Scientists across many therapeutic areas are striving to solve complex biological problems by measuring multiple analytes, thinking that together these data will power deeper discovery. However, analyzing these data independently is less effective and more time-consuming than analyzing them together. Cytobank’s new DROP feature allows scientists to apply machine learning algorithms to many data types, including these datasets, and to develop integrated insights quickly.
With bulk data, unsupervised machine learning algorithms on Cytobank can help you identify clinically relevant groups of samples by combining information from all of your markers at the same time. We’ll illustrate that here with Nanostring® 3D Biology™ technology, which simultaneously analyzes up to 800 SNVs, RNA, and proteins and phospho-proteins from the same sample. In this example, the assays used profile 104 SNV and small InDels, 192 RNA, and 28 total and phospho-proteins in 144 samples.
Run Cytobank’s machine learning-based dimensionality reduction and clustering tools across additional data types beyond cytometry. Discover biomarkers and explore cellular interactions and clinical outcomes faster and more comprehensively leveraging the scalable compute and collaborative power of the cloud.
Measuring system-wide immune responses requires significant breadth and depth of data [1, 2, 3]. Cytobank will soon release functionality on its Enterprise-level platform enabling you to expand your analysis beyond cytometry to tabular single cell or bulk data including RNA, DNA, extracted imaging features, proteins (e.g. cytokine/chemokine, antibodies, cellular proteins), metabolomics, clinical features, and more.
Analyze Multiple Single Cell Data Types to Discover More:
Leverage the discovery potential of broader, agnostic data types such as genomics and transcriptomics. Then cross-validate and delve deeper into mechanism with proteomics.
We recently hosted a very popular webinar, “Placing viSNE in Your Toolbox” featuring special guest Dr. Anna Belkina of Boston University School of Medicine. More than 500 investigators registered for our event to learn about cutting-edge tools and techniques for optimizing results from high-dimensional cytometry datasets.
Missed our latest viSNE presentation?
View the webinar recording here:
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Guest Blog by Tyler Burns
Tyler Burns is a Cancer Biology Ph.D Candidate in Dr. Garry Nolan’s lab at Stanford and a consultant for Cytobank. Tyler’s work in the Nolan lab is focused on developing novel computational methods for high-parameter single cell analysis.
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:
We were fortunate to attend the American Society of Hematology m
eeting earlier this month in San Diego. It brought together leading clinical scientists to present and discuss advances in malignant and non-malignant hematology.
Since the Cytobank platform is used by many who attended and presented, we were proud to see and be a small part of the exciting science. Highlights for us were: