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LUCIDus (version 3.0.3)

LUCID with Multiple Omics Data

Description

An implementation of estimating the Latent Unknown Clusters By Integrating Multi-omics Data (LUCID) model (Peng (2019) ). LUCID conducts integrated clustering using exposures, omics information (and outcome information as an option). This package implements three different integration strategies for multi-omics data analysis within the LUCID framework: LUCID early integration (the original LUCID model), LUCID in parallel (intermediate integration), and LUCID in serial (late integration). Automated model selection for each LUCID model is available to obtain the optimal number of latent clusters, and an integrated imputation approach is implemented to handle sporadic and list-wise missingness in multi-omics data. Lasso-type regularity for exposure and omics features were added. S3 methods for summary and plotting functions were fixed. Fixed minor bugs.

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Version

Install

install.packages('LUCIDus')

Monthly Downloads

282

Version

3.0.3

License

MIT + file LICENSE

Maintainer

Qiran Jia

Last Published

September 23rd, 2024

Functions in LUCIDus (3.0.3)

tune_lucid

A wrapper function to perform model selection for LUCID
estimate_lucid

Fit LUCID models with one or multiple omics layers
summary.lucid_parallel

Summarize results of the parallel LUCID model
gen_ci

generate bootstrp ci (normal, basic and percentile)
summary.early_lucid

Summarize results of the early LUCID model
simulated_HELIX_data

A simulated HELIX dataset for LUCID
plot

Visualize LUCID model through a Sankey diagram
sim_data

A simulated dataset for LUCID
predict_lucid

Predict cluster assignment and outcome based on LUCID model using new data of G,Z,(Y). If g_computation, predict cluster assignment, omics data, and outcome based on LUCID model using new data of G only This function can also be use to extract X assignment is using training data G,Z,Y as input.
Istep_Z

I-step of LUCID
print.sumlucid_early

Print the output of LUCID in a nicer table
boot_lucid

Inference of LUCID model based on bootstrap resampling
fill_data

Impute missing data by optimizing the likelihood function
check_na

Check missing patterns in one layer of omics data Z
lucid

Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data, allowing for tuning
print.sumlucid_parallel

Print the output of LUCID in a nicer table
print.sumlucid_serial

Print the output of LUCID in a nicer table
summary.lucid_serial

Summarize results of the serial LUCID model