Articles
Getting Started
- Getting Started
An introduction to the
metasnf
package and installation instructions.
Example Workflows
- A Simple Example
A minimal example of generating cluster solutions.
- A Complete Example
Step through a complex subtyping workflow.
Essential objects
- The Settings Matrix
The object that controls all hyperparameters defining the space of cluster solutions to explore.
- The Data List
The main object used to store data in the metasnf package.
Further customization
- SNF Schemes
Controlling the way that individual input dataframes are combined into a final fused network.
- Distance Metrics
Vary distance metrics to expand or refine the space of generated cluster solutions.
- Clustering Algorithms
Vary clustering algorithm to expand or refine the space of generated cluster solutions.
- Feature Weighting
Vary feature weights to expand or refine the space of generated cluster solutions.
Additional functionality
- Stability Measures
Evaluating robustness of cluster solutions through resampling methods.
- Quality Measures
Calculate context-agnostic measures of clustering compactness and separation.
- Confounders
Linearly regress out unwanted signal from features for clustering.
- Parallel Processing
Leverage parallel processing to speed up the metasnf pipeline.
- Label Propagation
Validate or extend cluster insights to new observations through semi-supervised label propagation.
- Imputations
Incorporate imputation approach as another source of variability in the generated space of cluster solutions.
- NMI Scores
Calculate how important various features were to the final SNF cluster solution.
Plotting
- Correlation Plots
Visualize correlations between data prior to clustering.
- Similarity Matrices
Visualize the affinity matrices produced by SNF and how they associate with other data attributes.
- Manhattan Plots
Visualize a summary of the association between cluster-feature and feature-feature relationships.
- Feature Plots
Visualize how features are distributed within a cluster solution.
- Alluvial Plots
Visualize how cluster number influences the distribution of observations.
Troubleshooting
- Troubleshooting
What to do when things aren’t working.