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Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.

ABSTRACT: Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict four clinical endpoints, and classification performances were compared in terms of accuracy, area under the curve, Matthews correlation coefficient and root mean-squared error. Whereas prediction performance varied with distinct clinical endpoints and classification models, equivalent performance metrics were observed for one- and two-color measurements in both internal and external validation. Furthermore, overlap of selected signature genes correlated inversely with endpoint prediction difficulty. In summary, our data strongly substantiate that the choice of platform is not a primary factor for successful gene expression based-prediction of clinical endpoints.

SUBMITTER: Oberthuer A

PROVIDER: S-EPMC2920066 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.

Oberthuer A A Juraeva D D Li L L Kahlert Y Y Westermann F F Eils R R Berthold F F Shi L L Wolfinger R D RD Fischer M M Brors B B

The pharmacogenomics journal 20100801 4

Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict ...[more]

PMID: 20676065

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Project description:BackgroundMicroarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.MethodsWe present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses.Results13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data.ConclusionsNormalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.

Project description:BackgroundThe availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.MethodsWe propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.ResultsWe apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality.ConclusionsOur results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

Dataset Information

Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.

Publications

Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.

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