Opls-da
Opls-da - Related Videos
03:50
ROS Detection with DCFH-DA Staining: Measuring Total ROS in Colorectal Cancer Cell Lines
0 Views •
2023
This video describes the protocol for detecting total cellular reactive oxygen species (ROS) using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA). This method helps visualize ROS localization in the adherent cells using a fluorescence microscope and further quantify ROS intensity with a fluorescence plate reader.
07:14
Investigating the 'Uncatchable Smile' in Leonardo da Vinci's La Bella Principessa: A Comparison with the Mona Lisa and Pollaiuolo's Portrait of a Girl
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Cited by 3 •
2016
This paper discusses the methodology used to reveal the 'Uncatchable Smile' illusion in Leonardo da Vinci's La Bella Principessa portrait. A combination of three methods was used (inter-observation, structured interviews, and psychophysical experiments), which led to an investigation that shaped itself without prior beliefs, thus reducing potential researcher bias.
06:16
White and Brown Adipose Grafts: An Approach to Correct Reproductive, Metabolic, and Renal Deficits in Black and Tan Brachyury (BTBR) Obese Mice
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2025
Here we present a white and brown adipose tissue transplantation protocol that offers a promising strategy to reverse subfertility, obesity, and hyperglycemia, while improving kidney function in diabetic and obese BTBR mice.
10:23
Standardization of a Cytometric Bead Assay Based on Egg-Yolk Antibodies
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Cited by 2 •
2023
This protocol describes a methodology for the preparation of latex beads for assays using IgY antibodies for antigen detection.
11:36
A Cell Culture Model for Studying the Role of Neuron-Glia Interactions in Ischemia
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Cited by 5 •
2020
Here, we present a simple approach using specific culture media that allows the establishment of neuron- and astrocyte-enriched cultures, or neuron-glia cultures from the embryonic cortex, with high yield and reproducibility.
FAQs
Predictive components contain variation in the chemical measurements that is correlated with predefined class membership. Orthogonal components contain systematic variation that does not contribute to that classification. Keeping these contributions separate helps distinguish chemically relevant class differences from other structured sources of variation, making multivariate results easier to interpret.
Chemical datasets can contain systematic variation unrelated to the condition or class being investigated. OPLS-DA assigns this variation to orthogonal components rather than allowing it to obscure class-correlated patterns. This separation can clarify which measured variables are associated with sample differences, particularly in complex spectroscopy, chromatography, and metabolomics datasets.
Adding excessive complexity can make the model capture patterns that appear to separate the predefined classes but do not represent reliable structure in the chemical data. This produces overfitting, in which apparent class separation may fail beyond the modeled observations. Appropriate validation is therefore necessary before treating the classification pattern as meaningful.
Researchers should assess model performance with appropriate validation rather than relying only on the apparent separation of predefined classes. Validation helps determine whether the modeled relationship is supported by the data rather than produced by excessive complexity. This step is especially important when interpreting chemical features as associated with different samples or conditions.
The method can simplify interpretation of complex data from spectroscopy, chromatography, and metabolomics. In these settings, many measured variables may vary together, making class-related patterns difficult to inspect directly. By partitioning predictive and orthogonal variation, the analysis can highlight chemical features associated with different predefined sample groups or experimental conditions.
A basic workflow begins by assigning measurements to predefined classes, modeling the chemical variables, and examining predictive and orthogonal components separately. Researchers then assess the model with appropriate validation before interpreting class-associated features. The final interpretation should connect the retained chemical patterns to differences between the samples or conditions under study.