CellView – Interactive Exploration Of High Dimensional Single Cell RNA-Seq Data

Advances in high-throughput single cell transcriptomics technologies have revolutionized the study of complex tissues. It is now possible to measure gene expression across thousands of individual cells to define cell types and states. While powerful computational and statistical frameworks are emerging to analyze these complex datasets, a gap exists between this data and a biologist’s insight. The CellView web application fills this gap by providing easy and intuitive exploration of single cell transcriptome data.

CellView enables cell type identification of clusters and discovery of
novel cell states in PBMC and pancreatic islet datasets

rna-seq

A. CellView’s graphical user interface has 3 different features that enables exploration of single cell RNA-seq datasets. B. Upon PBMC data upload, a 3D plot of cells clustered in t-SNE space is displayed in ‘overview’. Expression patterns of marker genes such as C. CD79A and D. CD3D can be visualized in multiple panels under the ‘Explore’ module assisting in cell type identification and to discover further heterogeneity. E. 3D display of cell type clusters identified in human pancreatic islets. F. Analysis using the ‘Co-expression’ module of CellView with marker genes aids in the identification the major endocrine cell populations, alpha (cluster 2), beta (cluster 3), gamma (cluster 5), delta (cluster 4) along with exocrine cell types like ductal (cluster 1), stellate (cluster 6), acinar (cluster 7) and endothelial (cluster 8) cells. Cluster and gene specific views, G. REG1A and H. TPP1 expression in the ductal cell cluster identifies cells in multiple states.

Availability – Access to CellView from: https://www.jax.org/CellOmics

Bolisetty MT, Stitzel ML, Robson P. (2017) CellView: Interactive Exploration Of High Dimensional Single Cell RNA-Seq Data. bioRXiv [Epub ahead of print]. [abstract]

 

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