Introduction | Latest: Cardinal 3.6 | Previous updates from Cardinal 3 | Previous updates from Cardinal 2 | Installation | Data import | Reading "continuous" imzML | Reading "processed" imzML | Data structures for MS imaging | MSImagingArrays: Mass spectra with differing m/z values | Accessing spectra arrays with spectraData() | Accessing pixel metadata with pixelData() | MSImagingExperiment: Mass spectra with shared m/z values | Accessing feature metadata with featureData() | Building from scratch | Visualization | Visualizing spectra with plot() | Visualizing images with image() | Region-of-interest selection | Saving plots and images | Dark themes | A note on plotting speed | Common operations on MS imaging datasets | Subsetting | Finding indices of mass spectra and images | Using subset() and friends | Slicing | Combining | Getters and setters | Summarization (e.g., mean spectra, TIC, etc.) | Loading data into memory | Coercion to/from other classes | Pre-processing | Normalization | Smoothing | Baseline subtraction | Recalibration | Peak processing | Peak picking | Peak alignment | Peak filtering | Peak picking based on a reference | Using peakProcess() | Binning | Example processing workflow | Data export | Parallel computing using BiocParallel | Using BPPARAM | Backend types | Getting available backends | Setting a parallel backend | RNG and reproducibility | Statistical methods | Session information

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

Introduction | Exploratory analysis | Principal components analysis (PCA) | Non-negative matrix factorization (NMF) | Feature colocalization | Image segmentation | Spatial shrunken centroids clustering | Spatial Dirichlet Gaussian mixture modeling | Classification and cross-validation | Projection to latent structures (PLS) | Spatial shrunken centroids classification | Class comparison | Sample-based means testing | Segment-based means testing | Session information

Introduction | Installation | Structure of imzML files | XML | Binary | Continuous | Processed | Additional notes | Parsing imzML files | Experimental metadata | File description | Scan settings | Software | Instrument configuration | Data processing | Spectrum metadata | Positions | m/z metadata | Intensity metadata | Mass spectra | Tracking experimental metadata | The ImzMeta class | Conversion between ImzML and ImzMeta | Minimum reporting guidelines | Writing imzML files | Writing a file from ImzML metadata | Writing a file from ImzMeta metadata | Session information