Integration of Multiple Data Sets with Clustering Techniques
ade4,a4Core, Biobase, cluster, plotrix, plyr, gplots,
gridExtra, limma,
gtools,e1071,pls,stats,utils,graphics,FactoMineR,analogue,lsa,
SNFtool,grDevices,ggplot2,circlize,Rdpack,data.table,igraph

Integration of Omics Data Using Linear Modeling
c(
person("Jalal", "Siddiqui", email = "Jalal.Siddiqui@osumc.edu", role="aut"),
person("Shunchao", "Wang", email="shunchao.wang@osumc.edu", role="aut"),
person("Rohith", "Vanam", email="rohith.vanam@osumc.edu", role="aut"),
person("Elizabeth", "Baskin", email="baskin.18@osu.edu", role="aut"),
person("Tara", "Eicher", email="tara.eicher@nih.gov", role=c("aut", "cre")),
person("Kyle", "Spencer", email="kyle.spencer@nih.gov", role="aut"),
person("Ewy", "Mathe", email="ewy.mathe@nih.gov", role="aut"))
This workflow takes analyte levels from two different types of analytes (e.g. gene expression and metabolite abundance), meta-information on each analyte type, and sample outcome and metadata to identify analyte pairs that are significantly associated with a continuous or discrete outcome (e.g. drug response or tumor type). The following references describe the methods in this package: (1) Jalal K. Siddiqui, et al. (2018) <doi:10.1186/s12859-018-2085-6>, (2) Andrew Patt, et al. (2019) <doi:10.1007/978-1-4939-9027-6_23>.
ComplexHeatmap, DT, ggplot2, graphics, grDevices, heatmaply,
highcharter, htmltools, KernSmooth, margins, methods, MASS,
RColorBrewer, reshape2, rmarkdown, shiny, shinydashboard,
shinyFiles, shinyjs, stats, testthat, utils
'AllClasses.R' 'internalfunctions.R' '01_ReadData.R'
'02_FilterData.R' '03_RunIntLim.R' '04_ProcessResults.R'
'IntLimResults_extendedfunctions.R' 'PermuteIntLIM.R'
'crossvalfunctions.R' 'plottingfunctions.R' 'runShiny.R'
'statsfunctions.R' 'writingfunctions.R'
Jalal Siddiqui [aut],
Shunchao Wang [aut],
Rohith Vanam [aut],
Elizabeth Baskin [aut],
Tara Eicher [aut, cre],
Kyle Spencer [aut],
Ewy Mathe [aut]

Numerical Integration for Integrated Likelihood
This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm.