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The Marker State Space (MSS) method for classifying clinical samples.

Fallon BP, Curnutte B, Maupin KA, Partyka K, Choi S, Brand RE, Langmead CJ, Tembe W, Haab BB - PLoS ONE (2013)

Bottom Line: Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers.Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies.MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Cancer Immunodiagnostics, Van Andel Institute, Grand Rapids, Michigan, USA.

ABSTRACT
The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

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Related in: MedlinePlus

Test set marker states and patient classifications.The same marker panel, thresholds, and classification rules as shown in figure 4 were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.
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pone-0065905-g004: Test set marker states and patient classifications.The same marker panel, thresholds, and classification rules as shown in figure 4 were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.

Mentions: The application of this three-marker panel to the test set achieved sensitivity of 85%, specificity of 96.2%, and accuracy of 89.4% (Fig. 4A). This performance held up well relative to the training set (even slightly improved) and was similar to that achieved by logistic regression (Table 1). The three-marker panel selected by logistic regression shared two markers in common with the MSS panel, and nearly all the samples were classified equivalently by MSS and logistic regression (data not shown). These analyses show that the MSS method can produce robust multi-marker panels that have consistent performance in cross validation and training set/test set analyses.


The Marker State Space (MSS) method for classifying clinical samples.

Fallon BP, Curnutte B, Maupin KA, Partyka K, Choi S, Brand RE, Langmead CJ, Tembe W, Haab BB - PLoS ONE (2013)

Test set marker states and patient classifications.The same marker panel, thresholds, and classification rules as shown in figure 4 were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3672150&req=5

pone-0065905-g004: Test set marker states and patient classifications.The same marker panel, thresholds, and classification rules as shown in figure 4 were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.
Mentions: The application of this three-marker panel to the test set achieved sensitivity of 85%, specificity of 96.2%, and accuracy of 89.4% (Fig. 4A). This performance held up well relative to the training set (even slightly improved) and was similar to that achieved by logistic regression (Table 1). The three-marker panel selected by logistic regression shared two markers in common with the MSS panel, and nearly all the samples were classified equivalently by MSS and logistic regression (data not shown). These analyses show that the MSS method can produce robust multi-marker panels that have consistent performance in cross validation and training set/test set analyses.

Bottom Line: Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers.Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies.MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Cancer Immunodiagnostics, Van Andel Institute, Grand Rapids, Michigan, USA.

ABSTRACT
The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

Show MeSH
Related in: MedlinePlus