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Data Management to Ensure Reproducibility
The analysis of flow cytometry and other single cell data requires a number of steps, such as compensation and other pre-processing tasks, gating and data visualization in different plot types as well as the export of statistical results for further analysis and graphing. This is typically done manually by individual researchers who may even use different tools for each of these different steps. As a result, the connection between primary data and final presented results is often not immediately obvious.
The Cytobank platform was specifically designed to address this challenge. For data analyzed on the Cytobank platform, it is always possible to drill down from a publication quality figure to the underlying primary data, thus ensuring that important knowledge on how scientific conclusions were drawn is retained and documented.
Figure 1. Link between Primary Data and Results on the Cytobank Platform.
The Experiment Manager on the Cytobank platform offers an interface that supports users in navigating their data by options to sort, filter and tag experiments. Linked experiments can be visualized in a tree-like hierarchy, enabling researchers to find related experiments quickly and to easily grasp the sequence of an analysis workflow.
Figure 2. Linked Experiments in Experiment Summary Page.
Enabling Collaboration to Accelerate DiscoveryInternational collaboration has become a driving force for scientific innovation. The Cytobank platform enables researchers around the globe to share their data and to collaborate on data analysis. Experiments can be grouped into Projects and collaborators can be assigned custom access permissions. Sample tags provide important contextual information that is accessible to everyone working on a data set and is no longer buried in individual lab notebooks. Additional information such as experimental protocols or results from other methods can be attached to the experiment on the Cytobank platform to create a central information repository.
Comprehensive Analysis of High Parametric DataData analysis packages for flow cytometry need to offer a number of functionalities, such as tools for compensation, gating and displaying data using different plot types. The Cytobank platform offers all of these prerequisites as well as fully integrated Machine Learning algorithms that enable an unbiased assessment of the data. Manual analysis of high dimensional data can be assumption-driven and may not allow the detection of unexpected effects. The unsupervised nature of clustering and dimensionality reduction tools such as viSNE, SPADE and FlowSOM increases the chance of discovering previously undescribed phenotypes.
Dynamic Figure Generation for Complex Data Sets
One of the main differentiators the Cytobank platform offers when it comes to the creation of figures from complex experiments is the use of sample tags to generate dynamic visualizations of results. Sample Tags become variables within the Working Illustration. These variables can be toggled on or off and rearranged dynamically to build and modify a figure. This allows one to work above the level of FCS file names, and instead use the scientific variables that were present in the experiment, to build a figure while retaining transparent access to the underlying experiment annotation and data processing steps.
Figure 3. Figure Generated Using Experimental Variables Stimulation Condition, Individuals as well as Gated Populations as Dimensions.