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Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons82090

Parida,  Shreemanta K.
Department of Immunology, Max Planck Institute for Infection Biology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons81969

Kaufmann,  Stefan H. E.
Department of Immunology, Max Planck Institute for Infection Biology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons81950

Jacobsen,  Marc
Department of Immunology, Max Planck Institute for Infection Biology, Max Planck Society;

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BMC_Bioinform_2010_11_27.pdf
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Citation

Repsilber, D., Kern, S., Telaar, A., Walzl, G., Black, G. F., Selbig, J., et al. (2010). Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach. BMC Bioinformatics, 11: 27.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-C032-5
Abstract
Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.