person("Floriane", "Larras", role = "ctb", email = "floriane.larras@inrae.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) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform 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 in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Larras et al (2018) <DOI:10.1021/acs.est.8b04752> at <https://hal.archives-ouvertes.fr/hal-02309919>.
Suggests: parallel, shiny, shinyBS, shinyjs, testthat, knitr, rmarkdown
Packaged: 2021-02-09 14:32:26 UTC; siberchicot

Packaged: 2021-09-29 14:08:33 UTC; siberchicot

Packaged: 2022-01-06 09:48:20 UTC; siberchicot

Title: Drug Repurposing in Cancer via a Subpathway Crosstalk Network
Authors@R: c(person(given = "Junwei", family = "Han", email = "hanjunwei1981@163.com", role  = c("aut", "cre", "cph")),
person(given = "Jiashuo", family = "Wu", role  = "aut"))
Description: A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) evaluating the eigenvector centrality (centrality score) of subpathways and identifying dysregulated subpathways in the subpathway-subpathway network, 2) evaluating the activity of subpathways by GSEA and weighting them with corresponding centrality score, 3) identifying optimal drugs for specific disease through constructing disease-drug reverse association based on the weighted activity of subpathways. There are also several functions used to visualize the results.
Imports: ChemmineR, GSVA, clusterProfiler, graphics, igraph, pheatmap,