About this app

How to explore your single-cell dataset?

You can explore this single-cell dataset in various ways.

Looking up cell-level gene expression (FeaturePlots)

In a first place you may just to review gene expression levels per cell. On many occassions it is helpful to split the display (using the splityBy drop-down menu) according to sampleIDs, cell cycle phase, condition or treatment.

Looking up cell-level gene-category expression (category FeaturePlots)

Similar to gene level expression levels, many experiments will have gene category level gene expression values available. Get a listing for those by typing cat_ into the gene name box.

Looking up category (e.g. cluster) level gene Expression (Violin Plots)

When dealing with lowly expressed genes, it is often helpful to switch the display to Violin plot by selecting a category option, such as cluster name, sample name or cell cycle phase as a-axis selection and, for example, log10 Expression as y-Axis selection.

Reviewing quality control parameters

To review quality parameters of the single-cell experiment, review measures for the number of RNA features as well as percentages of mitochondrial genes in your experiment by selecting the corresponding columns on the x- and y-axis.

Creating a data viewer for your single-cell dataset

If you have an R-Seurat object with your single-cell analysis, you can create a dataviewer similar to this one following the instructions in this github repository.