May 24, 2017  |  Announcements  |  By  |  0 Comments

Coming Soon: Analyze More Data Types in Cytobank

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 [123]. 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:image1

Leverage the discovery potential of broader, agnostic data types such as genomics and transcriptomics. Then cross-validate and delve deeper into mechanism with proteomics.

Analyzing more data types in Cytobank allows you to capitalize on more of the data you’re measuring in your studies. For example, identify populations within single cell data sets and then validate results with other data types. Start with single cell RNAseq data and identify a set of statistically significant population-specific biomarkers, then validate them with cytometry data and dive deeper into mechanistic protein studies. This approach allows you to start broad and agnostically and end up with a reduced set of markers for downstream repetitive use such as might be useful in clinical trials.

Applicable Cytobank Methods (single cell data): viSNE, SPADE, CITRUS

Purpose: Define populations of cells based on given markers

Results: Measure and visualize cell population abundance or marker expression; assess statistically significant differences between groups of samples


Analyze Bulk Data to Visualize Heterogeneity Between Samples

Analyze bulk data to identify groups of samples based on marker expression differences, and visualize whether the groups have any association with other outcomes such as clinical features (e.g. treatment arm or age). Pivoting the data, you can also ask whether there are groups of markers that are similar across samples, for example, to potentially reduce the number of markers you need to measure:

Analyze bulk data in Cytobank. A) Run viSNE on bulk data. Each dot represents one patient, and plots are colored by discrete and continuous variables including treatment arm, age, and marker. B) Combine bulk and single cell data in tabular format and run SPADE or viSNE to assess correlation of variables across both data types.

Applicable Cytobank Methods: viSNE, SPADE (for continuous variables)

Purpose: Define groups of samples based on biomarker signatures, visualize and compare markers that contribute to groups; visualize how groups are associated with other discrete or continuous variables

Expanded Data Types will be available to Enterprise-level license users.
Contact us at if you’d like to upgrade or learn more.


  1. Brodin P, Davis MM. Human immune system variation. Nat Rev Immunol. 2017 Jan;17(1):21–29. PMCID: PMC5328245
  2. Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Single-cell technologies for monitoring immune systems. Nat Immunol. 2014 Feb;15(2):128–135. PMCID: PMC4040085
  3. Blank CU, Haanen JB, Ribas A, Schumacher TN. The “cancer immunogram”. Science. 2016 May;352(6286):658-60. PMID: 27151852