Single-cell RNA sequencing has revolutionized ocular gene expression studies. This technology has enabled researchers to identify expression signatures for rare cell types and characterize how gene expression changes across biological conditions, such as topographic region or disease status. However, sharing single-cell RNA sequencing results remains a major obstacle, particular for individuals without a computational background.
To address these limitations, University of Iowa researchers developed Spectacle, an interactive web-based resource for exploring previously published single-cell RNA sequencing data from ocular studies. Spectacle is powered by a locally developed R package, cellcuratoR, which utilizes the Shiny framework in R to generate interactive visualizations for single-cell expression data. Spectacle contains five pre-processed ocular single-cell RNA sequencing data sets and is accessible via the web. With Spectacle, users can interactively identify which cell types express a gene of interest, detect transcriptomic subpopulations within a cell type, and perform highly flexible differential expression analyses. The freely-available Spectacle system reduces the bioinformatic barrier for interacting with rich single-cell RNA sequencing studies from ocular tissues, making it easy to quickly identify cell types that express a gene of interest.
Differential Expression in Spectacle
A-B. Differential expression can be performed between pre-characterized clusters of cells or interactively selected populations with the lasso tool, with cells selected on the left belonging to “Group_1” (A) and cells selected on the right belonging to “Group_2” (B). In addition to comparing expression between selected populations, differential expression can be performed between cells in the same region originating from different biological conditions, such as disease status (not shown). C. Differential expression results are displayed graphically. The y-axis depicts the log of the fold-change between cells in the Group_1 and Group_2 selections. The x-axis depicts a variable called “delta percent,” which represents the percentage of cells in Group_1 samples that express each gene minus the percent of cells in Group_2 samples that express the gene. For example, the gene BCO2 is expressed by 4.1% of cells in Group_1 and 67.6% of cells in Group_2, resulting in a delta percent of 0.041 minus 0.676 = −0.635. This visualization allows for the expression level (y-axis) and the proportion of expressing cells (x-axis) to be simultaneously evaluated. D. The cell selections are re-depicted on the standard dimensionality reduction space. E. In addition to graphical output, the differential expression results are displayed in tabular format, and can be exported to CSV or Excel files.