Introduction | Motivation | Overview | Installation | Preview of the NCI-60 dataset | Running and reviewing the MCIA output | Brief overview of the Global Scores Matrix ($F$) | Brief overview of the Global Loadings Matrix ($A$) | Part 1: Interpreting Global Factor Scores | nipals_multiblock() Generates Basic Visualizations | Visualizing a Factor Plot with Only Global Factor Scores | Visualizing the Clustering of Samples by Factor Scores | Part 2: Interpreting Global Loadings | Pseudoeigenvalues Representing the Contribution of Each Omic to the Global Factor Score | Visualize All Feature Loadings on Two Axes | Scree Plot: Visualizing the Top Features per Factor | Factor 1 | Factor 2 | Factor 4 | Pathway Analysis for the Top Factors using Data from Gene-Centric Omics Blocks | Gather Data and Generate the Report | Investigating the GSEA Summary Table | Session Info

Introduction | Installation | Vignette Pipeline | Data | All Sources | Bioconductor | 10x Genomics and Seurat | MCIA | Metadata | Running the decomposition | Visualization | Define colors | Eigenvalue scree plot | Projection plot | Global scores heatmap | Block weights heatmap | Loadings | Top features | Factor 1 | Factor 4 | Deep dive: Seurat analysis | Read in and process the data | Quality control | Metrics summary | GEX QC metrics | Before filtering | After filtering | Standard Seurat pipeline | Dimensionality reduction | Load in the processed object | PCA | UMAP | Marker overlays | Load marker genes | Dot plots | GEX | ADT | Feature plots | Violin plots | Annotate cell clusters | Annotations | UMAPs | Check the annotations | Save for MCIA | Session Info

Predicting MCIA global (factor) scores for new test samples | Installation | Split the data | Run nipalsMCIA on training data | Visualize model on training data using metadata on cancer type | Generate factor scores for test data using the MCIA_train model | Visualize new scores with old | Session Info