Beckman Coulter, Inc.

Antonio Arevalo Reyes

Jiuliu Lu

Zihua Wang

John Riley

Erik Alan Gustafson

A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element, a plurality of second input values associated with a second genetic element, and a plurality of third input values associated with a third genetic element associated with a plurality of samples. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples.

Systems and methods for detecting the presence of a biological status using clustering

Systems and methods for identifying Methicillin resistant strains of(MRSA) in a sample are used that are based on the fact that an MRSA positive sample should have roughly the same copy numbers of mecA, SCCmec, and a-specific target gene sequence. The systems and methods may further present the three assays simultaneously on a 2-D plot with each axis of the plot 120 degrees apart. According to one embodiment, a Y plot is used for the 2-D display. If a given sample has similar readings of mecA, SCCmec, and a-specific target gene sequence, the sample's measured copy numbers of mecA, SCCmec, and the-specific target gene sequence can plot close to the origin regardless of the sample's absolute assay readings. With the help of this transformation, a boundary function can be defined that can be used to distinguish MRSA-positive samples from MRSA-negative samples.

Systems and methods for detecting the presence of a biological status using plot

A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element (e.g., mecA), a plurality of second input values associated with a second genetic element (femA), and a plurality of third input values associated with a third genetic element (e.g., orfX) associated with a plurality of samples. Each sample includes a first input value in the plurality of first input values, a second input value in the plurality of second input values, and a third input value in the plurality of third input values. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples. The first and second boundary spaces differentiate a biological entity from other biological statuses. Other embodiments may also include the use of a genetic element such as SCCmec.

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Middleton, MA, United States of America

Antonio Arevalo Reyes