Roche Molecular Systems, Inc.

Cygnus, Inc.

Ventana Medical Systems, Inc.

Animas Corporation

Animas Technologies, LLC

Cyngnus, Inc.

Roche Diagnostics Gmbh

F. Hoffmann-la Roche Ag

Ronald T Kurnik

Janet A Tamada

Michael J Tierney

Russell O Potts

Timothy Christian Dunn

Matthew J Lesho

Charles W Wei

Steven Waterhouse

Yalia Jayalakshmi

Bret Berner

Jonathan J Oliver

Michael D Garrison

Kathleen C Farinas

Aditya P Sane

Rolf Knobel

Pascal Bamford

Martin Titz

Laurent Francioli

Jonathan M Baldanza

Jonathan James Oliver

Srinivas Chukka

Michael Otter

Anindya Sarkar

Lou Dietz

Soni Shukla

Bikash Sabata

Olcay Sertel

Christopher Elkin

Thomas Thurnherr

Jianmei Wang

Corey Hoeppner

Xianxian Liu

Chris Elkin

Aditya Sane

A single technique for determining Ct is provided that can be used for standard sigmoidal growth curves and for problematic growth curves, such as parabolic curves. The Ct value can be determined as the intersection of a line tangent to the growth curve at the maximum of the second derivative with a baseline of the growth curve. Such a Ct value is usable for sigmoidal curves and parabolic curves, and can provide linear calibration curves to achieve accuracy in determining initial concentrations of a sample.

Universal method to determine real-time PCR cycle threshold values

Accurate measurements of the presence or absence of a target cell in a sample are provided. For example, the sample can be mixed with a plurality of transduction particles capable of binding to the target cells, the transduction particles being engineered to include a nucleic acid molecule formulated to cause the target cells to produce a plurality of detectable reporter molecules once the particles bind to and deliver the nucleic acid molecules into the one or more target cells. A set of signal data points are received that are associated with a quantity of reporter molecules and the signal data points are analyzed to accurately detect target cells in the sample. Systems and methods are disclosed.

Systems and methods for detecting cells using engineered transduction particles

Systems and methods for removing jump discontinuities in growth data are provided. A first approximation to a received data set is determined by applying a non-linear regression process to a non-linear function that models the data set to determine parameters, including a step discontinuity parameter. A second approximation to the data set is also determined by applying a regression process to a second non-linear function to determine parameters, including a step discontinuity parameter, of the second function. One of the approximations is selected based on an information coefficient determined for each of the approximations. If a confidence interval for the step discontinuity parameter includes zero, no correction is made, and if includes zero, then a correction is made. For a correction, the portion of the data curve prior to the step change is replaced with appropriate portion of the selected approximation to produce a shift-corrected data set.

Systems and methods for step discontinuity removal in real-time PCR fluorescence data

Processing of images acquired via fluorescence microscopy by identifying broadband and other undesired signals from the component signals of a scanned image, and processing selected regions of the image that are known to contain signals of interest, thereby extracting or identifying desired signals while subtracting undesired signals. One or more broadband signals are recognized by their unique signature and ubiquitous dispersion through the image. Regions of the scanned image may be tagged as consisting of predominantly broadband signals and are ignored during a spectral unmixing process. The remaining regions of the image, or selected regions of the image known to contain desired signals, may be unmixed, and the plurality of reference spectra subtracted from the components to extract or identify the target signals. The set of target signals may be refined by eliminating known or obvious sources of noise by, for instance, being compared to known or ideal sets of signals from similar materials.

Spectral unmixing

Systems, methods, and apparatuses are provided for detecting and potentially invalidating or correcting jump errors in data from growth processes. A jump error can be identified by determining a second derivative of the data set, and identifying two consecutive cycles with opposite signs in the second derivative. Once a jump error has been detected, the data set can be invalidated or corrected based on various criteria. Whether to invalidate or correct can be based on an absolute jump height, a relative jump height (e.g., relative to the net growth or relative to the baseline), an absolute location (cycle number) of the jump, or a relative location. In one implementation, the jump can be corrected by subtracting a jump height from points subsequent to the jump or by adding the jump height to points prior to the jump.

Detection and correction of jumps in real-time PCR signals

A method and system for spectral demultiplexing of fluorescent species, such as quantum dots, conjugated with a biological tissue. The process of demultiplexing involves a non-liner regression based on curve-fitting of estimated spectra of the quantum dots and confidence intervals describing the parameters of such fitting curve for typical quantum dots.

Method and system for spectral unmixing of tissue images

Systems and methods for removing jump discontinuities in PCR or growth data. A first approximation to a curve that fits a received data set is determined by applying a non-linear regression process to a non-linear function that models the data set to determine parameters, including a step discontinuity parameter, of the non-linear function. One example of a non-linear function is a double sigmoid equation. A second approximation to a curve that fits the data set is also determined by applying a regression process to a second non-linear function to determine parameters, including a step discontinuity parameter, of the second function. One of the first or second approximations is then selected based on an information coefficient determined for each of the first and second approximations. If a confidence interval calculated for the step discontinuity parameter includes the value zero, no step correction is made. If the confidence interval does not include the value zero, then a step correction is made. If a step correction is made, the portion of the data curve prior to the step change is replaced with appropriate portion of the selected approximation to produce a shift-corrected data set. In certain aspects, the portion of the data curve up to the first point after the step change is corrected. In certain aspects, if the approximation does not satisfy a goodness of fit criterion, no step correction is made. The shift-corrected data set is returned and may be displayed or otherwise used for further processing.

Numerical determinations of the first derivatives of a melt curve data set are made. A model function, such as a Gaussian Mixture Model (GMM) function, with parameters determined using a Levenberg-Marquardt (LM) regression process is used to find an approximation to the first derivative curve. The maximum values of the numerically determined first derivative values are used as initial conditions for parameters of the model function. The determined parameters provide one or more fractional melting temperature values, which can be returned, for example, displayed or otherwise used for further processing.

Determination of melting temperatures of DNA

Systems and methods for determining melting temperatures, Tm, for DNA from melt curve data. The systems and methods also allow for quantitative determination of gene amount based on peak height. A PCR analogy is used to perform quantization of an acquired melting curve dataset. The melting curve is transformed using a horizontal flip and a horizontal translation, and a double sigmoid equation is then fit to the data. Inverse translation and inverse horizontal flip transforms are applied to the equation to produce an equation based solution of the melt curve dataset. The equation based solution of the melt curve is then used to determine the first derivative (e.g., Tm value) and peak height.

Determination of single peak melting temperature by PCR analogy and double sigmoid equation

Systems and methods for processing PCR curves, and for identifying the presence of a parabolic-shaped PCR curve. Use of a piecewise linear approximation of a PCR curve enables a more realistic elbow value to be determined in the case of parabolic shaped PCR curves.

Determination of elbow values for PCR for parabolic shaped curves

Numerical determinations of the first derivatives of a melt curve data set are made. A baseline is determined for the first derivative values and the baseline is subtracted from the first derivative values to produce modified first derivative values. A first maximum value of the modified first derivative values is determined and said first maximum value represents a melting temperature Tm of a DNA sample. A model function, such as a Gaussian Mixture Model (GMM) function, with parameters determined using a Levenberg-Marquardt (LM) regression process can also be used to find an approximation to the first derivative curve. The maximum values of the numerically determined first derivative values are used as initial conditions for parameters of the model function. The determined parameters provide one or more fractional melting temperature values, which can be returned, for example, displayed or otherwise used for further processing.

Systems and methods for determining a transition value in a sigmoid or growth curve, such as the end of the baseline region or the elbow value or Ct value of a PCR amplification curve. Numerical determinations of the second derivatives and curvature values of a PCR data set are made. A Gaussian Mixture Model (GMM) function with parameters determined using a Levenberg-Marquardt (LM), or other, regression process is used to find an approximation to the second derivative values and to the curvature values, where the maximum values of the numerically determined second derivative values and/or curvature values are used as initial conditions for parameters of the GMM function. The determined parameters provide fractional Ct values. The Ct value(s) are then returned and may be displayed or otherwise used for further processing.

Real-time PCR elbow calling by equation-less algorithm

Systems and methods for determining cross-talk coefficients in curves, such as sigmoid-type or growth curves, and PCR curves and nucleic acid melting curves in particular, as well as for applying the cross-talk coefficients to produce cross-talk corrected data sets using a linear subtractive model. Cross-talk signal coefficients are determined using cross-talk data acquired across the entire signal acquisition range. Analyzing across all of the signal curve data provides for a more robust cross-talk correction across the entire data acquisition range. A linear subtractive model is used to correct data sets having cross-talk components.

Systems and methods for determining cross-talk coefficients in PCR and other data sets

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Foster City, CA, United States of America

Ronald T Kurnik