Package: CardinalWorkflows 1.37.1

Introduction | Segmentation of a pig fetus wholy body cross section | Pre-processing | Visualization | Ion images | Principal components analysis (PCA) | Non-negative matrix factorization (NMF) | Segmentation with spatial shrunken centroids (SSC) | Plotting the segmentation | Plotting the (shrunken) mean spectra | Plotting and interpretting t-statistics of the m/z values | Retrieving the top m/z values | Segmentation of a cardinal painting | Segmentation with SSC | Session information

Introduction | Classification of a renal cell carcinoma (RCC) cancer dataset | Pre-processing | Visualization | Ion images | Principal components analysis (PCA) | Non-negative matrix factorization (NMF) | Classification with spatial shrunken centroids (SSC) | Cross-validation with SSC | Plotting the classified images | Plotting the (shrunken) mean spectra | Plotting and interpretting t-statistics of the m/z values | Retrieving the top m/z-values | Multiple instance learning (MIL) | Preparing the samples (bags) | Cross-validation with MIL | Additional notes on cross-validation | Session information

Introduction | Statistical testing for a renal cell carcinoma (RCC) cancer dataset | Pre-processing | Non-specific filtering to reduce data size | Segmentation with spatial Dirichlet Gaussian mixture model (DGMM) | Class comparison with means-based testing | Fitting models with means-summarized groups | Interpreting the results | Class comparison with segmentation-based testing | Fitting models with spatial-DGMM-summarized groups | Session information