Introduction | Installation | 1. Download the package from Bioconductor | 2. Load the package into R session | Quick start: DESeq2 | Conduct principal component analysis (PCA): | A scree plot | A bi-plot | Quick start: Gene Expression Omnibus (GEO) | A pairs plot | A loadings plot | An eigencor plot | Access the internal data | Advanced features | Determine optimum number of PCs to retain | Modify bi-plots | Colour by a metadata factor, use a custom label, add lines through origin, and add legend | Supply custom colours and encircle variables by group | Stat ellipses | Change shape based on tumour grade, remove connectors, and add titles | Modify line types, remove gridlines, and increase point size | Colour by a continuous variable and plot other PCs | Quickly explore potentially informative PCs via a pairs plot | Determine the variables that drive variation among each PC | Correlate the principal components back to the clinical data | Plot the entire project on a single panel | Make predictions on new data | Acknowledgments | Session info | References

Introduction | Installation | 1. Download the package from Bioconductor | 2. Load the package into R session | Quick start | Plot the most basic volcano plot | Advanced features | Modify cut-offs for log2FC and P value; specify title; adjust point and label size | Adjust colour and alpha for point shading | Adjust shape of plotted points | Adjust cut-off lines and add extra threshold lines | Adjust legend position, size, and text | Fit more labels by adding connectors | Only label key variables | Draw labels in boxes | Italicise labels and flip volcano on it's side | Over-ride colouring scheme with custom key-value pairs | Over-ride colour and/or shape scheme with custom key-value pairs | Encircle / highlight certain variables | Highlighting key variables via custom point sizes | Change to continuous colour scheme | Custom axis tick marks | Acknowledgments | Session info | References

Introduction | Installation | 1. Download the package from Bioconductor | 2. Load the package into R session | Tutorial 1: CyTOF FCS data | Perform principal component analysis (PCA) | Perform UMAP | Create a contour plot of the UMAP layout | Show marker expression across the layout | Shade cells by metadata | Find ideal clusters in the UMAP layout via k-nearest neighbours | Plot marker expression per identified cluster | Determine enriched markers in each cluster and plot the expression signature | Disease vs Healthy metacluster abundances | Treatment type metacluster abundances | Expression signature | Tutorial 2: Import from Seurat | Tutorial 3: Import any numerical data | Acknowledgments | Session info | References