What alona is

alona is a web-based bioinformatics service designed to analyze single cell RNA sequencing (scRNA-seq) data. This service performs processing, analysis and visualization using some of the most popular scRNA-seq algorithms. Cells are annotated into cell types using marker genes from PanglaoDB. Instead of using the default marker genes, the user can also provide such a list. Processed data can be explored from an intuitive web interface based on JavaScript.

An example of scRNA-seq data from the mouse brain. See below for more examples.

The service is free and it is provided by the Integrated Cardio Metabolic Centre at the Karolinska Institutet .

alona is based on the adobo Python package

How it works

The flowchart to the left illustrates the basic steps in the analysis. The boxes in yellow illustrate steps performed by alona.

A gene expression from a scRNA-seq experiment is accepted as input. Data should have been mapped and reads counted using external software.

The uploaded data can come from any single cell protocol or platform.

The analysis usually completes within 10 to 15 minutes on a dataset consisting of 5000 cells. An e-mail can be sent as a reminder.

alona generates an archive of files in tar.gz format, containing analysis results and plots.

How to prepare your data

Only a gene expression matrix is needed with genes as rows and cells as columns. Single cell gene expression matrices are often large because thousands of cells are usually sampled. The matrix is either a plain text file, having any arbitrary filename, and it must be compressed using zip, gzip, bzip2 or xz. The correct filename extension must be used for the corresponding compression (i.e. zip for zip, gz for gzip, bz2 for bzip2, and xz for xz).

Uploaded data must not be normalized; i.e., measurements should be raw read counts.

File name: The file name is only allowed to contain the following types of characters: letters (a-z), numbers (0-9), dashes (-), underscores (_), white spaces, and dots (.).

Detailed description on data preparation from sequences

alona assumes that you have already performed basic data processing (alignments, read counting, etc). See here for our guide on how to preparing a dataset for analysis with alona.

Supported data formats

  • A compressed plain text gene expression matrix in one of the following formats:
    • gzip
    • bzip2
    • zip
    • xz
  • Matrix market (format described here). This is a popular sparse format used in NCBI's GEO. If this format is chosen, there should be three gzip-compressed files loaded in a single tar.gz archive and the files must have exactly these names: matrix.mtx.gz, barcodes.tsv.gz and genes.tsv.gz

Example datasets

Can be used for testing.

View in alona GEO Accession Species Tissue
Open GSE95315 Mus musculus Brain
Open GSE129798 Mus musculus Kidney
Open GSE122465 Mus musculus Bone marrow
Open GSE129218 Mus musculus Skin
Open GSE145443 Mus musculus Epididymis and vas deferens
Open GSE132771 Human Lung

Privacy notice

When your data are uploaded, a cookie is stored in your browser session and it identifies your session. When your browser cache is cleared, you will not be able to see your data any further; as an optional reminder, an email can be sent when data analysis has completed. In the future we might limit how long time data are stored before being automatically deleted. While the "dashboard" is private and specific to your session, the web browser interface to your processed data is public and not protected by login; that means that anyone that knows the full URL to your dataset can view it. An optional password can be set when uploading data to provide additional security.

Standalone version

As mentioned above, this web server is based on adobo — a Python package for single cell RNA-seq analysis. See github.com/oscar-franzen/adobo. The Python code for the performed analysis is included in the attached archive after alona has finished running, and the template can be downloaded here.


In average, a dataset runs for 21 minutes.

Current system status

Queued datasets: 0 Running datasets: 0