File input


Data structure

This navigation panel shows the structure of a Seurat object.


Assays contain matrices with sequencing derived counts.

In the most basic form of scRNA-seq those are transcript counts. However, also antibody-derived counts are stored in assays. The slots of each assay contain matrices (for raw, normalized and scaled data) as well as information on the features (e.g. highly variable features).


Metadata is stored in vectors (usually factor or numeric) with information about the cells (columns).


Nearest Neighbors

Dimensional Reduction Objects

Dimensional reduction techniques are used mainly for summarization (e.g. PCA, diffusion maps) or visualization (e.g. tSNE, UMAP)


A container for data storage. We store tables to convert between different gene identifiers (e.g. ENSEMBL, SYMBOL) here

Project name

Name of your project. Can contain a small explanation of your project






Quality Control
Feature Selection
Expression summaries

What is the single-cell browser?

The single-cell browser is an r-shiny application for exploratory analysis of sequencing-derived single-cell data.


This application takes data from single-cell sequencing (e.g. scRNA-seq) contained in an R object from class Seurat or SingleCellExperiment and automatically produces visualizations for tasks such as quality control, feature selection and the exploration of low-dimensional embeddings.

To this end, all computationally intensive tasks such as dimensional reduction or clustering have to be performed in advance.


The main page contains an upload panel and the data structure. The structure is based on the Seurat object (version 3) and gives a quick overview of the data that is currently contained in the object. Example data is available and can be used as a reference.

This application has been developed by Oliver Dietrich at the Saliba Lab at the Helmholtz Institute for RNA-based Infection Research (HIRI)

Funded by the German Research Society (DFG) through the GRK2157 (3D Infect).