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‎_articles/RJ-2024-012/RJ-2024-012.R‎

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# Generated by `rjournal_pdf_article()` using `knitr::purl()`: do not edit by hand
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# Please edit RJ-2024-012.Rmd to modify this file
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## ----fig1, echo=FALSE , fig.cap="Three DAGs represent how three different types of the LUCID model integrate genetic/environmental exposures ($\\boldsymbol{\\mathbf{G}}$), other multi-omics data ($\\boldsymbol{\\mathbf{Z}}$), and the phenotype trait ($\\boldsymbol{\\mathbf{Y}}$). (a) LUCID early integration; (b) LUCID in parallel; (c) LUCID in serial. The squares represent observed data, the circles represent unobserved latent variables (clusters) and model parameters, and the diamond refers to $L_1$ penalty terms for regularization for (a). $\\boldsymbol{\\mathbf{CoG}}$ and $\\boldsymbol{\\mathbf{CoY}}$ represent covariates to be adjusted in the LUCID model. Missingness is allowed in multi-omics data. $\\boldsymbol{\\mathbf{Z}}$ is divided into subsets of observations with complete measurements and observations with missingness. For (b) and (c), $\\boldsymbol{\\mathbf{Z}}$ is partitioned into $m$ layers.", fig.alt="graphic without alt text", fig.show='hold', fig.align="center", out.width="100%"----
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knitr::include_graphics(c("figures/fig1.png"))
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## ----table-1, echo = FALSE, results = 'asis'----------------------------------
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table_1_data <- read.csv("table_data_1.csv")
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knitr::kable(table_1_data, caption=" Functions in the LUCIDus package. lucid() calls estimate_lucid() and tune_lucid() in the backend. The two workhorse functions are not normally called directly, but they can be useful when a user wants to examine the model fitting process in more detail.")
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## ----fig2, echo=FALSE , fig.cap="The workflow of the LUCIDus package. Dark blue nodes represent input data, light blue nodes represent output results. Green nodes and dashed arrows are optional steps for model estimation. Red texts correspond to 5 key functions in LUCIDus. Steps and functions marked with an asterisk currently work for LUCID early integration only.", fig.alt="graphic without alt text", fig.show='hold', fig.align="center", out.width="100%"----
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knitr::include_graphics(c("figures/fig2.png"))
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## ----fig3, echo=FALSE , fig.cap="An example of using the Sankey diagram to visualize a LUCID model. The dark grey nodes represent the exposure, the light grey node represents the outcome, the blue nodes are omics data and the orange nodes are latent clusters. The width of links and nodes corresponds to effect size. Light-colored links represent negative associations while dark-colored links indicate positive associations.", fig.alt="graphic without alt text", fig.show='hold', fig.align="center", out.width="100%"----
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knitr::include_graphics(c("figures/fig3.png"))
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## ----fig4, echo=FALSE , fig.cap="Choosing optimal number of latent clusters K based on BIC.", fig.alt="graphic without alt text", fig.show='hold', fig.align="center", out.width="100%"----
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knitr::include_graphics(c("figures/fig4.png"))
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## ----fig5, echo=FALSE , fig.cap="Two simulated missing patterns in methylomics data", fig.alt="graphic without alt text", fig.show='hold', fig.align="center", out.width="100%"----
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knitr::include_graphics(c("figures/fig5.png"))
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‎_articles/RJ-2024-012/RJ-2024-012.Rmd‎

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‎_articles/RJ-2024-012/RJ-2024-012.html‎

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‎_articles/RJ-2024-012/RJ-2024-012.tex‎

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‎_articles/RJ-2024-012/RJ-2024-012.zip‎

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‎_articles/RJ-2024-012/RJournal.sty‎

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% Package `RJournal' to use with LaTeX2e
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% Copyright (C) 2010 by the R Foundation
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% Copyright (C) 2013 by the R Journal
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%
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% Originally written by Kurt Hornik and Friedrich Leisch with subsequent
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% edits by the editorial board
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%
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% CAUTION:
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% Do not modify this style file. Any changes to this file will be reset when your
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pdftitle={The R Journal Volume \RJ@volume/\RJ@number, \RJ@month \RJ@year},%
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pdfauthor={R Foundation for Statistical Computing},%
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The \raisebox{-8pt}{\includegraphics[height=77pt]{Rlogo-5}}\hspace{10pt}
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Journal
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{\large \hfill Volume \RJ@volume/\RJ@number, \RJ@month{} \RJ@year \quad}
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{\Large A peer-reviewed, open-access publication of the \\
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R Foundation for Statistical Computing}
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