For experiments in which analyzed samples come from different classes or conditions, a common goal of supervised analysis is classification: given a labeled training set for which classes are already known, we want to predict the class of a new sample.
Unlike unsupervised analysis such as segmentation, classification requires biological replicates for testing and validation, to avoid biased reporting of accuracy. Cardinal provides cross-validation for this purpose.
In this vignette, we present an example classification workflow using Cardinal.
We begin by loading the package:
This example uses DESI spectra collected from a renal cell carcinoma (RCC) cancer dataset consisting of 8 matched pairs of human kidney tissue. Each tissue pair consists of a normal tissue sample and a cancerous tissue sample. The goal of the workflow is to develop classifiers for predicting whether a new tissue sample is normal or cancer.