Sparse Extended Analysis of Modules

Documentation for package ‘seam’ version 0.1.0

compute_activity	Compute activity statuses of variables using SPCA
compute_module_trait_correlations	Compute Pearson's correlations between clinical traits and sparse or classic eigengenes
compute_variable_trait_correlations	Compute variable–trait correlations
insight_correlations_lollipop	Lollipop plot of variable-trait correlations and SPCA-weighted correlations
insight_correlations_reconstruct	Reconstruct module-trait correlations from top contributing variables
insight_correlations_sign_changes	Visualize variable-trait correlations and negative SPCA weights
insight_weights_lollipop	Compare SPCA variable weights between two groups
plot_activity_bar	Plot variable activity statuses as a stacked barplot
plot_activity_bubble	Plot variable activity statuses as a bubble chart
plot_explained_variance	Plot explained variance in modules as a heatmap
plot_lambdas	Plot SPCA sparsity and explained variance across different lambda values
plot_module_heatmaps	Plot module correlation heatmaps and sparsified heatmaps
plot_module_trait_correlations	Plot module-trait correlations for sparse and classic eigengenes
plot_sparsity	Plot percentage of inactive variables (sparsity) across modules as a heatmap
seam	seam: Sparse Eigengene Analysis of Modules
seam_demo	Simulated expression and trait data for two groups and two modules
spca_all_modules	Perform SPCA across groups and modules
spca_all_modules_grid	Perform SPCA across groups and modules with module-specific sparsity