bioRxivBioinformaticsPreprint
OmicPioneer-sc: an integrated, interactive visualization environment for single-cell sequencing data
John N. WeinsteiniD, Mary A. Rohrdanz, Mark Stucky, James M. MelottiD, Jun MaiD, Vakul MohantyiD, Ganiraju C. ManyamiD, Christopher WakefieldiD, Ken CheniD, Nicholas E. NaviniD, Michaël RyaniD, Rehan AkbaniiD, Bradley McIntosh BroomiD
Abstract
OmicPioneer-sc is an open-source data visualization/analysis package that integrates dimensionality-reduction plots (DRPs) such as t-SNE and UMAP with Next-Generation Clustered Heat Maps (NGCHMs) and Pathway Visualization Modules (PVMs) in a seamless, highly interactive exploratory environment. It includes fluent zooming and navigation, a statistical toolkit, dozens of link-outs to external public bioinformatic resources, high-resolution graphics that meet the requirements of all major journals, and the ability to store all metadata needed to reproduce the visualizations at a later time. A user-friendly, multi-panel graphical interface enables non-informaticians to interact with the system without programming, asking and answering questions that require navigation among the three types of modules or extension from them to the Gene Ontology or information on therapies. The visual integration can be useful for detective work to identify and annotate cell-types for color-coding of the DRPs, and multiple NGCHMs can be layered on top of each other (with toggling among them) as an aid to multi-omic analysis. The tools are available in containerized form with APIs to facilitate incorporation as a plug-in to other bioinformatic environments. The capabilities of OmicPioneer-sc are illustrated here through application to a single-cell RNA-seq airway dataset pertinent to the biology of both cancer and COVID-19. [Supplemental material is available for this article.]
- organism: human
- assay: scRNA-seq
- research focus: clustering
- research focus: cell type annotation
- research focus: dimensionality reduction
- research focus: software / tool
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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