Orthogonal Partial Least Squares
Orthogonal Partial Least Squares - Related Videos
01:26
Orthogonal Trajectories
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2026
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
06:50
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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2019
The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.
10:32
Orthogonal Protein Purification Facilitated by a Small Bispecific Affinity Tag
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Cited by 7 •
2012
A novel and highly efficient two-step affinity chromatography protocol has been developed and is described in detail. The method is based on a small purification tag with two inherent affinities and is applicable to a wide range of target proteins with different properties.
08:43
Calibration Procedures for Orthogonal Superposition Rheology
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Cited by 4 •
2020
We present a detailed calibration protocol for a commercial orthogonal superposition rheology technique using Newtonian fluids including end-effect correction factor determination methods and recommendations for best practices to reduce experimental error.
00:57
Root Mean Square
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2023
If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero. For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
FAQs
The decomposition assigns different latent variables according to their relationship with the response. Predictive components capture variation in the predictor matrix that covaries with the outcome, whereas orthogonal components represent systematic variation independent of that outcome. This separation allows investigators to focus on outcome-associated information without treating every structured difference in the data as medically relevant.
Systematic variation in clinical, metabolomic, proteomic, or genomic measurements may exist without explaining the response of interest. Modeling that variation in orthogonal components keeps it visible while distinguishing it from predictive signal. This distinction helps researchers interpret complex patient data more selectively and reduces the risk of confusing unrelated structure with disease or treatment-associated patterns.
Score and loading plots provide visual tools for examining the latent-variable model. They help investigators inspect how observations and measured variables are organized within the extracted components and how those patterns relate to the response. In medical studies, these plots can support interpretation of patient characteristics, disease-associated patterns, or variables contributing to treatment-related differences.
Researchers begin with a predictor matrix containing measurements such as clinical, metabolomic, proteomic, or genomic variables and a response describing the outcome of interest. OPLS then decomposes the predictors into predictive and orthogonal components. Investigators interpret the resulting latent variables through score or loading plots to identify patterns associated with the response.
OPLS is useful when a study contains many predictor variables and the research question concerns their relationship with a response. Medical researchers may apply it to investigate disease-associated patterns, treatment response, or patient characteristics. Its multivariate structure helps summarize complex measurements while retaining variation relevant to the outcome, supporting interpretation of high-dimensional datasets.
The analysis can support biomarker discovery, patient classification, and hypothesis generation. By distinguishing predictive from unrelated systematic variation, it helps identify patterns linked with a disease, treatment response, or patient characteristic. These results provide an interpretable basis for recognizing associations in complex datasets, although the model serves as an analytical and exploratory tool rather than a standalone clinical conclusion.
In biomarker research, OPLS can highlight measured variables whose patterns covary with a medical response while separating other structured variation. In patient classification, the latent representation can organize multivariable measurements around response-associated patterns. Applied to clinical, metabolomic, proteomic, or genomic data, this supports the search for informative signatures and the development of testable medical hypotheses.