Example GWAS data | Navigation | Add recombination rate | Add LD via LDlink | Overlay eQTL data via LDlink | Dual GWAS browsing | Saving plots | Citation | References

Introduction | Installation | Example locus plot | Accessing Ensembl databases | Controlling the locus | Obtaining LD information | Add recombination rate | Customise plots and labels | Point shapes | Customise gene tracks | Plot gene annotation only | Add eQTL data via LDlink | Change y-axis variable | Arrange multiple locus plots | Layering plots | Column of plots | Overlaid plots | Add custom legend / features | ggplot2 version | Shapes | Arrange multiple ggplots | Plotly version | Plot all GWAS peaks | Manhattan & other plots | Citation | References

Geometric single cell deconvolution | Installation | Algorithm | Example dataset | h5ad file | Seurat file | Obtain gene signatures | Extract cell clusters | Create gene signature | Visualise gene signature | Signature diagnostics | Refine gene signature | Deconvolution | Sampled pseudo-bulk | Real bulk RNA-Seq settings | Tuning deconvolution | Simulating noise | Deconvolution diagnostics | Standard error | Confidence intervals | Residual gene expression | Multipass outlying gene removal | Variable compensation | Citation

Variable importance & stability | Ranking variables | Explainable AI with Shapley values | One-hot encoding for categorical predictors | Troubleshooting | Citation | References

Brain Atlas | Neurons | Non-neuronal cells | Merging signatures | Simulating pseudobulk | Merging simulated data | Deconvolution

Installing from CRAN | Installing from Github | Installing Locally | Overview | Metadata | Count data | Dispersion | Size Factors | Fitting Models | Outputs | Q-values | glmmTMB models | Gaussian mixed effects models | Hypothesis testing | Extracting individual Genes | Refitting a different model | Model Plots | Fold change plots | MA plots | Troubleshooting | Citing glmmSeq | References

Introduction | Variable selection | Installation | Examples | Importance of nested CV | Nested CV with glmnet | One-hot encoding (dummy variables) | Filters | Custom filter | Missing data | Nested modification of predictors | Class imbalance | Custom balancing function | Nested CV with caret | Notes on caret | Stratifying CV folds | Bayesian shrinkage models | Extra performance metrics | Precision-recall curves | Making predictions | Repeated nested CV | Parallelisation | How many cores? | Troubleshooting | Citation | References

Introduction | Installation | Scatter plots | Export plot to PDF | Save state | Export SVG from plotly | Export as plotly object | Colour | Shape | Size | Choosing label names | Controlling axes | Axis titles | Axis limits and outliers | Axis tick marks | Gridlines | Legends | Further control of plotting | panel.last | plotly_filter | Volcano plots | MA plots | Expanding full gene names | Colour schemes | Titles | Adding left/right subtitles | Setting P value cut-off | Complex colour schemes | Controlling labels | Label line and text | Adding label boxes | Matching label & point colours | Manhattan plot | Locus plots | Known issues

Parallelisation | Linear regression with a Bayesian shrinkage model (continuous outcomes) | Logistic regression with a Bayesian shrinkage model (binary outcomes) | Note | Troubleshooting | Citation | References

Installation | Examples | mclapply with progress bar | Tracking subprogress | foreach | Printing from parallel code