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LUCIDus (version 2.2.1)

Latent Unknown Clustering Integrating Multi-View Data

Description

An implementation of the LUCID model (Peng (2019) ). LUCID conducts integrated clustering using exposures, omics data (and outcome as an option). An EM algorithm is implemented to estimate MLE of the LUCID model. LUCIDus features integrated variable selection, incorporation of missing omics data, bootstrap inference, prediction and visualization of the model.

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Install

install.packages('LUCIDus')

Monthly Downloads

282

Version

2.2.1

License

GPL-3

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Maintainer

Yinqi Zhao

Last Published

November 8th, 2022

Functions in LUCIDus (2.2.1)

boot.lucid

Deprecated function boot.lucid
print.lucid

Print the output of est_lucid
tune_lucid

A wrapper function to perform model selection for LUCID
print.sumlucid

Print the output of LUCID in a nicer table
summary_lucid

Summarize results of LUCID model
sim_data

A simulated dataset for LUCID
predict_lucid

Predict cluster assignment and outcome based on LUCID model
plot_lucid

Visualize LUCID model through a Sankey diagram
Istep_Z

I-step of LUCID
gen_ci

generate bootstrp ci (normal, basic and percentile)
helix_data

HELIX data
check_na

Check missing patterns in omics data Z
fill_data

Impute missing data by optimizing the likelihood function
est.lucid

Deprecated function est.lucid
lucid

Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data
est_lucid

Fit LUCID model to conduct integrated clustering
boot_lucid

Inference of LUCID model based on bootstrap resampling