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Outcome prediction based on microarray analysis: a critical perspective on methods.

Zervakis M, Blazadonakis ME, Tsiliki G, Danilatou V, Tsiknakis M, Kafetzopoulos D - BMC Bioinformatics (2009)

Bottom Line: The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods.This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set.The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr

ABSTRACT

Background: Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.

Results: A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance.

Conclusion: Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

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Principal component analysis. Three-Dimensional Principal Component Analysis (PCA) on the three examined data sets reveals that breast cancer (A) demonstrates the largest degree of overlap between the two classes, with colon cancer (C) coming next and leukemia (B) being the easiest one in locating a decision boundary.
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Figure 1: Principal component analysis. Three-Dimensional Principal Component Analysis (PCA) on the three examined data sets reveals that breast cancer (A) demonstrates the largest degree of overlap between the two classes, with colon cancer (C) coming next and leukemia (B) being the easiest one in locating a decision boundary.

Mentions: To highlight differences between the three data sets, a three-dimensional principal component analysis (PCA) depicted in Figure 1 was performed, which could assist us in estimating a decision boundary. Such an analysis revealed that the BC data set perhaps demonstrates the highest degree of overlap between the two classes. CC data set appears to be more separable than BC, while the leukemia data set appears to be most easily distinguishable.


Outcome prediction based on microarray analysis: a critical perspective on methods.

Zervakis M, Blazadonakis ME, Tsiliki G, Danilatou V, Tsiknakis M, Kafetzopoulos D - BMC Bioinformatics (2009)

Principal component analysis. Three-Dimensional Principal Component Analysis (PCA) on the three examined data sets reveals that breast cancer (A) demonstrates the largest degree of overlap between the two classes, with colon cancer (C) coming next and leukemia (B) being the easiest one in locating a decision boundary.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667512&req=5

Figure 1: Principal component analysis. Three-Dimensional Principal Component Analysis (PCA) on the three examined data sets reveals that breast cancer (A) demonstrates the largest degree of overlap between the two classes, with colon cancer (C) coming next and leukemia (B) being the easiest one in locating a decision boundary.
Mentions: To highlight differences between the three data sets, a three-dimensional principal component analysis (PCA) depicted in Figure 1 was performed, which could assist us in estimating a decision boundary. Such an analysis revealed that the BC data set perhaps demonstrates the highest degree of overlap between the two classes. CC data set appears to be more separable than BC, while the leukemia data set appears to be most easily distinguishable.

Bottom Line: The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods.This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set.The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr

ABSTRACT

Background: Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.

Results: A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance.

Conclusion: Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

Show MeSH
Related in: MedlinePlus