Package: molnet
Type: Package
Title: Predicting Differential Drug Response using Multi-Omics Networks
Version: 0.1.0
Authors@R: 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"))
    )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Imports: igraph, dplyr, stringr, WGCNA, Rfast, readr, tibble, tidyr,
        magrittr, rlang
Suggests: rmarkdown, knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2021-08-05 15:40:14 UTC; julianhugo
Author: 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>)
Maintainer: Katharina Baum <katharina.baum@hpi.de>
Repository: CRAN
Date/Publication: 2021-08-06 08:30:02 UTC
Built: R 4.2.0; ; 2022-04-13 17:05:04 UTC; unix
