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Biostatistical Service and Support

We provide statistical support for all scientific activities at the DKFZ, from in vitro and animal to human subject studies. Our support covers experimental design, sample size/power estimation, data analysis, software guidance, visualization and interpretation of statistical results, and preparation of results for publication. It ranges from brief statistical consultations to long-term collaborations and covers standard statistical analysis approaches as well as the development of complex statistical methods tailored to specific questions. We offer discussions on advantages and disadvantages of different statistical methods and guidance for the method of choice in specific cases.We provide assistance on statistical aspects and requirements of funding applications, ethical vote applications, clinical trial protocols and animal studies. For those interested in learning more about statistical methods, we offer several statistics courses at the DKFZ.

How to prepare data

For standard experiments (no high-throughput measurements) recorded in spreadsheet files, samples/observations/replicates should be entered in rows, features/characteristics in columns. If multiple measurements per sample have been made (e.g. time series), each measurement should go into a separate row and an identifier variable for samples should be included. Column names should not contain any special signs. If measurements are coded, a legend must be provided. Dates should all be in the same format. If during the process of analysis your data must be updated or corrected, please provide an updated file without changing column names, formats etc. Information supplied by highlighting, coloring or any other type of formatting cannot be imported and used for the analysis.


The DKFZ provides SPSS SigmaPlot for standard analysis in a user-friendly environment. GraphPad Prism is another user-friendly statistical software frequently used at the DKFZ but without a campus-wide license. The Genomics and Proteomics Core Facility provides bioinformatics  tools for conducting standard microarray/sequencing analysis, such as Chipster and IPA. Our division generally uses R/Bioconductor and SAS for power/sample- size estimations.

Reproducible Research

We consider reproducible research to be essential for scientific work. For this reason, we prepare our analysis in R/Bioconductor in combination with Sweave/Knitr in order to allow for reproducibility of results, figures and tables. If requested, we can also provide stand-alone analysis scripts that can be used to reproduce results and can be submitted along with your manuscript.

Support for PhD students

We encourage PhD candidates and their supervisors to contact us whenever they need statistical advice on their experimental design, the methods to use, the correct application of statistical software, or the proper interpretation of results. We normally expect PhD candidates to perform the statistical analyses for their theses themselves. Of course, in case of a more complex analysis requiring advanced statistical knowledge and/or software expertise we will provide the necessary support.

How to contact us?

Please email the division of Biostatistics at and briefly describe your experiment/question and your aim.

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