Amber Biosciences offers consulting services in bioinformatics and bio-statistics.

We work interactively with our customers and take part in their research projects.

Contact us either at or personally.

Contact information can be found at Contact.

Consulting fee

We usually have fixed prices for our projects because it is easier for customers to handle.

We will agree on a price after initial discussions.

Our consulting services

How we work

We work interactively with our customers and try to listen to their needs. The usual procedure is that we have an initial meeting where we discuss the project. After agreeing on a contract, we perform the analysis in close contact with the customer. Our work is done when there is agreement that a final result has been achieved.

Gene expression

We have a lot of experience working with gene expression data sets from microarrays or qPCR. We can help with every step in the analysis including finding new scientific hypothesis.


We also analyze proteomics data from mass spectrometry or 2D gels.


We can take raw sequencing data and pre-process it into a format for further analysis. Sequencing data can be used for gene expression as well as an alternative to microarrays.


We perform supervised classification using support vector machines and other techniques. Classification is useful for diagnostics. For example, blood samples might contain diagnostic information. Supervised classification might be able to find the diagnostic signal in the data.


We can perform various statistical tests on the data. We have used R and Matlab, but we can help with other systems as well.


We prepare and customize publication ready figures.

Data repositories

Many journals require that data is deposited in a public repository. We handle the entire process of formatting and uploading the data.


Most data requires pre-processing and quality control. We can perform such processing when necessary.


We can compare various normalization strategies and help select a proper normalization.
hierarchical clustering