MPI für Infektionsbiologie / Department of Immunology |
|Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach|
|Authors:||Repsilber, Dirk; Kern, Sabine; Telaar, Anna; Walzl, Gerhard; Black, Gillian F.; Selbig, Joachim; Parida, Shreemanta K.; Kaufmann, Stefan H. E.; Jacobsen, Marc|
|Date of Publication (YYYY-MM-DD):||2010-01-14|
|Title of Journal:||BMC Bioinformatics|
|Journal Abbrev.:||BMC Bioinformatics|
|Sequence Number of Article:||27|
|Copyright:||© 2010 Repsilber et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|Abstract / Description:||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.|
|Comment of the Author/Creator:||The Grand Challenges in Global Health Project: Grant Number 37772, “Biomarkers of protective immunity against Tuberculosis in the context of HIV/AIDS in Africa”, was funded by a grant from the Bill & Melinda Gates Foundation through the Grand Challenges in Global Health Initiative.|
|External Publication Status:||published|
|Communicated by:||Hilmar Fünning|
|Affiliations:||MPI für Infektionsbiologie/Department of Immunology|
|External Affiliations:||Res Inst Biol Farm Anim, Dept Genet & Biometry, D-18196 Dummerstorf, Germany.; Univ Potsdam, Inst Biochem & Biol, Bioinformat Chair, D-14476 Potsdam, Germany.; Univ Stellenbosch, ZA-7505 Cape Town, South Africa.; Bernhard Nocht Inst Trop Med, Dept Immunol, D-20359 Hamburg, Germany.|
|Identifiers:||ISI:000275199300001 [ID No:1] |
ISSN:1471-2105 [ID No:2]
The scope and number of records on eDoc is subject
to the collection policies defined by each institute
- see "info" button in the collection browse view.