Analytics Features
Analytics Features Introduction
Overview of the analytics features on this page.
| Feature | Utility Overview | Documentation | Video Tutorial |
|---|---|---|---|
| Average Plot with Original Spectra | Visualize original data, visualize standard deviation and mean spectrum | Average Plot | |
| Confidence Interval Plot | Visualize standard deviation and mean spectrum | Confidence Interval Plot | |
| Spectra Derivation | Visualize the first and second derivatives of your data | Spectra Derivation | |
| Correlation Heatmap | Quantify similarities between spectra trends in a grid format | Correlation Heatmap | |
| Peak Identification and Stats | Identify and classify peaks in spectra intensity | Peak Identification | |
| Gaussian Peak Fitting | Find a set of Gaussian curves that fit to the peaks in your data | Gaussian Peak Fitting | |
| Hierarchically-clustered Heatmap | Group spectra with similar spectra, visualize differences with a color mapping | Clustermap | |
| Principal Component Analysis (PCA) | Simplify data by reducing it to a small list of components, useful for identifying noise | PCA | |
| T-Distributed Stochastic Neighbor Embedding (t-SNE) | Visualize clustering of high-dimensional data in 2D | T-SNE |
Please find more detailed information in child documents.