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>.
| Version: |
2.0.2 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
ComplexHeatmap, DT, ggplot2, graphics, grDevices, heatmaply, highcharter, htmltools, KernSmooth, margins, methods, MASS, RColorBrewer, reshape2, rmarkdown, shiny, shinydashboard, shinyFiles, shinyjs, stats, testthat, utils |
| Suggests: |
knitr |
| Published: |
2022-08-22 |
| Author: |
Jalal Siddiqui [aut],
Shunchao Wang [aut],
Rohith Vanam [aut],
Elizabeth Baskin [aut],
Tara Eicher [aut, cre],
Kyle Spencer [aut],
Ewy Mathe [aut] |
| Maintainer: |
Tara Eicher <tara.eicher at nih.gov> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
IntLIM results |