Explore Noncoding Visualization and Analysis (NOVA)
| Abbreviations Key | |
| EMR | electronic medical record |
| HISE | Human Immune System Explorer |
| IDE | integrated development environment |
| NOVA | noncoding visualization and analysis |
| PBMC | peripheral blood mononuclear cell |
| scRNA–seq | small cytoplasmic RNA sequencing |
At a Glance
The Noncoding Visualization and Analysis (NOVA) tool helps you derive new insights from pipeline results without writing any code. You can select samples or datasets of interest and generate visualizations that help you analyze and present your data in new ways and accelerate the generation of new hypotheses.
Description
Without performing any complex coding tasks, you can use NOVA to visualize and explore your data, share it with others, and save your output. For example, you could display a single data set to depict gene expression associated with various metadata fields, or you could do a side-by-side comparison of two different data sets.
Abstractions and visualizations
In HISE, the terms abstraction and visualization are defined as follows:
- Abstraction: The layout of a visualization, minus any associated data
- Visualization: An interactive application that combines a data set with an abstraction
Abstractions are developed within the HISE IDE. When you develop an abstraction, you can save the finished file to HISE. Then you (or any other HISE user) can do an advanced search, choose your own data sets, and generate an interactive visualization based on your saved abstraction.
Capabilities
By defining the visualization template and specifying the structure of the data set, NOVA separates the data from the code responsible for creating visualizations. In addition to visualizing existing files, you can choose files within HISE and perform real-time, on-demand data analysis and visualization. After you combine an abstraction with data to generate a visualization, you can discard or save the result. Saved visualizations can then be published.
Data sources
You can use NOVA to visualize human metadata, flow cytometry data, or scRNA-seq data. In an advanced search, a subjects or samples query yields a human metadata visualization. A results files query yields a visualization of the flow cytometry supervised gating pipeline. For details, see Use Built-in Visualization Tools.
| Data set | Description | Additional information |
| Human metadata | Includes survey results, laboratory results, and EMR data. | You can search for metadata at the subject level, which includes subject demographics and EMR data. "Subject demographics" refers to data such as biological sex, birth yea, and ethnicity. You can also search for metadata at the sample level, which means subject demographics, CBC (lab) results, and survey data. "CBC data" can refer to any related lab results. "Survey data" refers to a participant's questionnaire data. Both CBC and survey results are tied to the sample—in other words, these results are associated with a particular research visit. |
| Flow cytometry data | Derived from a technique in which a laser is used to detect and measure physical and chemical characteristics of a population of cells or particles. | In a per sample and per panel visualization, you can select cell subsets, removing the unselected cells from view. In addition, you can select results of the same type (including panel) of multiple samples and compare the results side by side. You can segment the data in multiple ways, such as by subject demographics, sample metadata, and CBC data, depending on the nature of your samples. |
scRNA-seq data | Derived from small cytoplasmic RNA sequencing. | With certain limitations, labeled data from a single sample can be loaded into a heatmap. |
Saving and Sharing Your Visualization
When you visualize a data set that addresses your research questions, you can save the visualization to a study and share your results with other scientists. When you commit the visualization to a study, HISE records the IDE instance you used, including your input data, the underlying code, the resulting data used to render the visualization, and the visualization itself. (The actual home/Jupyter directory, however, is not saved.) The resulting trace is saved to a study so that you and other study users can return to it later. The study shows the relationship between the visualization and its associated IDE instance (if any). This detailed record of your development environment, data, and methods enables other scientists to reproduce your findings reliably and consistently. For details, see .
Output
Each visualization is available in two forms. The first is a static image that can be used as a hero image or inserted into a report, such as a Google Doc. The second is the original interactive visualization file. Any study user can load the visualization directly in the UI to explore the data.
When a visualization is saved to a study, it's saved in draft mode by default. You can use this stage to review the results with your collaborators. If you aren't satisfied with the visualization, you can delete it and post an improved version. When you're happy with the result, you can save it as the final version. Only final visualizations can be attached to publications or used in Data Apps.