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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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Assigning patient classes and classifying marker states.(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.
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pone-0065905-g001: Assigning patient classes and classifying marker states.(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.

Mentions: The Marker State Space (MSS) method operates on a binary system in which each individual marker is either high (1) or low (0), based on a threshold for that marker (Fig. 1A). The state space is the combinations of 1s and 0s that are possible for a certain number of markers. A panel of two markers has four possible states: 0,0; 0,1; 1,0; and 1,1; and a panel of three markers has eight possible states: 0,0,0; 0,0,1; 0,1,0; 0,1,1; 1,0,0; 1,0,1; 1,1,0; and 1,1,1 (Fig. 1B). (Panels with more markers would have 2n possible states, n being the number of markers.) A given sample occupies exactly one state, depending on its pattern of high and low values for each marker. In order to classify samples, each state is designated as either a “case” state or a “control” state. For example, in a two-marker panel, the state 0,0 could indicate control samples, and the states 0,1; 1,0; and 1,1 could indicate case samples (Figs. 1C and 1D).


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)

Assigning patient classes and classifying marker states.(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3672150&req=5

pone-0065905-g001: Assigning patient classes and classifying marker states.(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.
Mentions: The Marker State Space (MSS) method operates on a binary system in which each individual marker is either high (1) or low (0), based on a threshold for that marker (Fig. 1A). The state space is the combinations of 1s and 0s that are possible for a certain number of markers. A panel of two markers has four possible states: 0,0; 0,1; 1,0; and 1,1; and a panel of three markers has eight possible states: 0,0,0; 0,0,1; 0,1,0; 0,1,1; 1,0,0; 1,0,1; 1,1,0; and 1,1,1 (Fig. 1B). (Panels with more markers would have 2n possible states, n being the number of markers.) A given sample occupies exactly one state, depending on its pattern of high and low values for each marker. In order to classify samples, each state is designated as either a “case” state or a “control” state. For example, in a two-marker panel, the state 0,0 could indicate control samples, and the states 0,1; 1,0; and 1,1 could indicate case samples (Figs. 1C and 1D).

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