Research article
Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach
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* Corresponding author: Dirk Repsilber repsilber@fbn-dummerstorf.de
1 Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany
2 Bioinformatics Chair, Institute for Biochemistry and Biology at the University of Potsdam, Karl-Liebknecht-Str. 24-25, D 14476 Potsdam-Golm, Germany
3 Molecular Biology and Human Genetics, University of Stellenbosch, Tygerberg, Cape Town 7505, South Africa
4 Department of Immunology, Max-Planck-Institute for Infection Biology, Charitéplatz 1, D 10117 Berlin, Germany
5 Department of Immunology, Bernhard-Nocht-Institute for Tropical Medicine, Bernhard-Nocht-Str. 74, D 20359 Hamburg, Germany
BMC Bioinformatics 2010, 11:27 doi:10.1186/1471-2105-11-27
Published: 14 January 2010Additional files
Additional file 1:
R-package deconf(Windows) including example data and script. R-package deconf (Windows version) which implements the deconfounding algorithm together with options for normalization, run-time options for the iteration process, and number of cell-type specific gene expression profiles to be estimated. Also, some toy examples and part of the experimental dataset are included together with executable example scripts for demonstration purposes.
Format: ZIP Size: 1.2MB Download file
Additional file 2:
R-package deconf(tar-gz archive). R-package deconf (tar-gz archive)
Format: GZ Size: 1.2MB Download file