LUCID with Multiple Omics Data

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Documentation for package ‘LUCIDus’ version 3.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

analyze_missing_pattern	Describe the missing-data pattern of an omics matrix
boot_lucid	Inference of LUCID model based on bootstrap resampling
check_imputation_quality	Check whether imputed values are distributionally plausible
check_na	Classify each subject's omics missingness pattern
estimate_lucid	Fit LUCID models with one or multiple omics layers
get_cluster_assignment	Extract the hard cluster assignment from a fitted LUCID model
get_selected_G	Extract selected (retained) exposures from a fitted LUCID model
get_selected_Z	Extract selected (retained) omics features from a fitted LUCID model
get_top_omics_features	Extract the top-N most important omics features from a fitted LUCID model
lucid	Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data, allowing for tuning
plot.early_lucid	Visualize an early-integration LUCID model through a Sankey diagram
plot.lucid_parallel	Sankey diagram for a parallel-integration LUCID model (not yet implemented)
plot.lucid_serial	Sankey diagram for a serial-integration LUCID model (not yet implemented)
plot_cluster_omic_profile	Plot per-cluster omics profiles
predict_lucid	Predict Cluster Assignment and Outcome From a Fitted LUCID Model
print.sumlucid_early	Print the output of LUCID in a nicer table
print.sumlucid_parallel	Print the output of LUCID in a nicer table
print.sumlucid_serial	Print the output of LUCID in a nicer table
safe_impute	Single-value imputation that tolerates fully missing columns
simulated_HELIX_data	A simulated HELIX dataset for LUCID
sim_data	A simulated dataset for LUCID
summary.early_lucid	Summarize results of the early LUCID model
summary.lucid_parallel	Summarize results of the parallel LUCID model
summary.lucid_serial	Summarize results of the serial LUCID model
summary_lucid	Summarize results of the early LUCID model
tune_lucid	Wrapper for LUCID Model and Penalty Tuning