TOP
TOP Constructs Transferable Model Across Gene Expression Platforms
Bioconductor version: 3.23 · Package version: 1.12.0
TOP constructs a transferable model across gene expression platforms for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The TOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("TOP") Details
| Maintainer | Harry Robertson <harry.robertson@sydney.edu.au> |
| Author | Harry Robertson [aut, cre] (ORCID: <https://orcid.org/0000-0001-9203-3894>), Nicholas Robertson [aut] |
| License | GPL-3 |
| URL | https://github.com/Harry25R/TOP |
| Bug Reports | https://github.com/Harry25R/TOP/issues |
| Downloads rank | 214 |
| Source branch | RELEASE_3_23 |
| biocViews | GeneExpression, Software, Survival |
Documentation
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Dependencies
Depends: R (>= 4.1.0)
Imports: assertthat, caret, ClassifyR, directPA, doParallel, dplyr, ggnewscale, ggplot2, ggraph, ggrepel, ggthemes, glmnet, Hmisc, igraph, latex2exp, limma, magrittr, methods, plotly, pROC, purrr, reshape2, stats, stringr, survival, tibble, tidygraph, tidyr, statmod
Suggests: knitr, rmarkdown, BiocStyle, Biobase, curatedOvarianData, ggbeeswarm, ggsci, survminer, tidyverse
Reverse dependencies
Suggests Me (1): ClassifyR