LUCIDus: Integreted clustering with multi-view data

The LUCIDus package implements the statistical method LUCID proposed in the research paper A Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) with Phenotypic Traits (Bioinformatics, 2020). LUCID conducts integrated clustering by using multi-view data, including exposures, omics data with/without outcome. LUCIDus features variable selection, incorporating missingness in omics data, visualization of LUCID model via Sankey diagram, bootstrap inference and functions for tuning model parameters.

If you are interested in integration of omic data to estiamte mediator or latent structures, please check out Conti Lab to learn more.

Installation

You can install the stable release from CRAN by:

install.packages("LUCIDus")

or install the development version of LUCIDus from GitHub with:

# install.packages("devtools")
devtools::install_github("USCbiostats/LUCIDus")

# install package with vignettes
devtools::install_github("USCbiostats/LUCIDus", build_vignettes = TRUE)

Usage

Please refer to the tutorial.

Citation

#> 
#> To cite LUCID methods, please use:
#> 
#>   Cheng Peng, Jun Wang, Isaac Asante, Stan Louie, Ran Jin, Lida Chatzi,
#>   Graham Casey, Duncan C Thomas, David V Conti (2019). A latent unknown
#>   clustering integrating multi-omics data (LUCID) with phenotypic
#>   traits. Bioinformatics, btz667. URL
#>   https://doi.org/10.1093/bioinformatics/btz667
#> 
#> To cite LUCIDus R package, please use:
#> 
#>   Yinqi Zhao (2022). LUCIDus: an R package to implement the LUCID
#>   model. CRAN. R package version 2.2 URL
#>   https://yinqi93.github.io/LUCIDus/index.html
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

Acknowledgments

• Dr. Lida Chatzi and awesome researchers from Chatzi Lab
• Dr. Nikos Stratakis
• Dr. Cheng Peng
• USC IMAGE Group (Supported by the National Cancer Institute at the National Institutes of Health Grant P01 CA196569)