Package: DRaWR
Title: Discriminative Random Walk with Restart
Version: 1.0.3
Authors@R: person("Charles", "Blatti", email = "blatti@illinois.edu", role = c("aut", "cre"))
Author: Charles Blatti [aut, cre]
Maintainer: Charles Blatti <blatti@illinois.edu>
Description: We present DRaWR, a network-based method for ranking genes or
properties related to a given gene set. Such related genes or properties are
identified from among the nodes of a large, heterogeneous network of biological
information. Our method involves a random walk with restarts, performed on
an initial network with multiple node and edge types, preserving more of the
original, specific property information than current methods that operate
on homogeneous networks. In this first stage of our algorithm, we find the
properties that are the most relevant to the given gene set and extract a
subnetwork of the original network, comprising only the relevant properties. We
then rerank genes by their similarity to the given gene set, based on a second
random walk with restarts, performed on the above subnetwork.
Depends: R (>= 3.0)
License: GPL-2
LazyData: true
Imports: Matrix, ROCR, methods
RoxygenNote: 5.0.1.9000
NeedsCompilation: no
Packaged: 2022-03-24 13:56:52 UTC; blatti
Repository: CRAN
Date/Publication: 2022-03-24 14:10:02 UTC

Authors@R: c(person("Marie-Laure", "Delignette-Muller", role = "aut", email = "marielaure.delignettemuller@vetagro-sup.fr"),
person("Elise", "Billoir", role = "aut", email = "elise.billoir@univ-lorraine.fr"),
person("Floriane", "Larras", role = "ctb", email = "floriane.larras@ufz.de"),
person("Aurelie", "Siberchicot", role = c("aut", "cre"), email = "aurelie.siberchicot@univ-lyon1.fr"))
Description: Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations, at least 6, and the more the better) rather than a great number of replicates (no need of three replicates). 'DRomics' provides functions 1) to check and normalize data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data transformed in log2, or another type of data that can be directly fitted by least-square regression without any normalization step. In the future this tool will also be able to process RNA-seq data. For further details see Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.
Depends: R (>= 3.4.0), limma, utils, grDevices
Suggests: parallel, shiny, shinyBS, shinyjs, testthat