c(person("James D.", "Pampush", role = c("aut", "cre", "cph"), email = "jdpampush@gmail.com"),
person("Julia M.", "Winchester", role = c("aut", "cph")),
person("Paul E.", "Morse", role = c("aut", "cph")),
person("Alexander Q.", "Vining", role = c("aut", "cph")),
person("Edward", "Fuselier", role=c("aut", "cph")))
James D. Pampush [aut, cre, cph],
Julia M. Winchester [aut, cph],
Paul E. Morse [aut, cph],
Alexander Q. Vining [aut, cph],
Edward Fuselier [aut, cph]

c(person("James D.", "Pampush", role = c("aut", "cre", "cph"), email = "jdpampush@gmail.com"),
person("Paul E.", "Morse", role = c("aut", "cph")),
person("Alexander Q.", "Vining", role = c("aut", "cph")),
person("Edward", "Fuselier", role=c("aut", "cph")))
R (>= 2.10), alphahull, rgl, Rvcg, pracma
James D. Pampush [aut, cre, cph],
Paul E. Morse [aut, cph],
Alexander Q. Vining [aut, cph],
Edward Fuselier [aut, cph]

Multivariate Outlier Detection in Contingency Tables
Outlier detection in, possibly high-dimensional, categorical data following
Mads Lindskou et al. (2019) <doi:10.1111/sjos.12407>.
Rcpp, doParallel, foreach, ggplot2, ggridges, ess
knitr, rmarkdown, pander, dplyr, testthat, igraph

Predicting Differential Drug Response using Multi-Omics Networks
c(
person(given = "Katharina",
family = "Baum",
email = "katharina.baum@hpi.de",
role = c("cre"),
comment = c(ORCID = "0000-0001-7256-0566")),
person(given = "Julian",
family = "Hugo",
email = "julian.hugo@student.hpi.de",
role = c("aut"),
comment = c(ORCID = "0000-0003-3355-1071")),
person(given = "Spoorthi",
family = "Kashyap",
email = "spoorthi.kashyap@student.hpi.de",
role = c("aut")),
person(given = "Nataniel",
family = "Müller",
email = "nataniel.mueller@student.hpi.de",
role = c("aut"),
comment = c(ORCID = "0000-0002-0275-3992")),
person(given = "Justus",
family = "Zeinert",
email = "justus.zeinert@student.hpi.de",
role = c("aut"),
comment = c(ORCID = "0000-0003-3918-0507"))
)
Networks provide a means to incorporate molecular interactions into
reasoning, but on the omics-level, they are currently mainly used to combine
genomic and proteomic information. We here present a novel network analysis
pipeline that enables integrative analysis of multi-omics data including
metabolomics. It allows for comparative conclusions between two different
conditions, such as tumor subgroups, healthy vs. disease, or generally control
vs. perturbed.
Our approach focuses on interactions and their strength instead of on node
properties and includes molecules with low abundance and unknown function. We
use correlation-induced networks that are reduced and combined to form
heterogeneous, multi-omics molecular networks. Prior information such as
metabolite-protein interactions are incorporated. A semi-local, path-based
integration step denoises the network and ensures integrative conclusions. As
case studies, we investigate differential drug response in breast cancer tumor
datasets providing proteomics, transcriptomics, phospho-proteomics and
metabolomics data and contrasting patients with different estrogen receptor
status.
Our proposed pipeline leverages multi-omics data for differential predictions,
e.g. on drug response, and includes prior information on interactions.
The case study presented in the vignette uses data published by
Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
to the software and explicitly not to the included data.
igraph, dplyr, stringr, WGCNA, Rfast, readr, tibble, tidyr,
magrittr, rlang
Katharina Baum [cre] (<https://orcid.org/0000-0001-7256-0566>),
Julian Hugo [aut] (<https://orcid.org/0000-0003-3355-1071>),
Spoorthi Kashyap [aut],
Nataniel Müller [aut] (<https://orcid.org/0000-0002-0275-3992>),
Justus Zeinert [aut] (<https://orcid.org/0000-0003-3918-0507>)