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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# ampir <a href='https://github.com/Legana/ampir'><img src="figures/ampir_hex.png" width="90" align="right" height="100" /></a>
# AMP prediction
This repository contains code used for the [ampir](https://github.com/Legana/ampir) R package paper published in the *Bioinformatics* journal as [btaa653](https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btaa653/5873588)
- [Collate data](01_collate_databases.md) : Rmd file [01_collate_databases.Rmd](01_collate_databases.Rmd)
- [Build training sets](02_build_training_data.md) : Rmd file [02_build_training_data.Rmd](02_build_training_data.Rmd)
- [Feature selection analysis](03_feature_selection.md) : Rmd file [03_feature_selection.Rmd](03_feature_selection.Rmd)
- [Model tuning](04_tune_model.md) : Rmd file [04_tune_model.Rmd](04_tune_model.Rmd)
- [Benchmarking](05_benchmark.md) : Rmd file [05_benchmark.Rmd](05_benchmark.Rmd)
- [Case studies](06_case_studies.md) : Rmd file [06_case_studies.Rmd](06_case_studies.Rmd)
- [Test ampir_v1.1.0](test_models.md) : Rmd file [test_models.Rmd](test_models.Rmd)
The files required to run the code in these Rmd files can be obtained [here](http://data.qld.edu.au/public/Q5999/Legana/AMP_pub/data.tgz) or by using the command:
```bash
wget 'http://data.qld.edu.au/public/Q5999/Legana/AMP_pub/data.tgz' -O data.tgz
tar -zxvf data.tgz
```
### `sessionInfo()`
```
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.3.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pROC_1.18.0 ampir_1.1.0 cowplot_1.1.1 ggpubr_0.4.0 forcats_0.5.1 stringr_1.4.0
[7] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 tidyverse_1.3.1
[13] caret_6.0-90 lattice_0.20-45 ggplot2_3.3.5
loaded via a namespace (and not attached):
[1] nlme_3.1-153 fs_1.5.2 lubridate_1.8.0 httr_1.4.2 tools_4.1.2
[6] backports_1.4.1 utf8_1.2.2 R6_2.5.1 rpart_4.1-15 DBI_1.1.2
[11] colorspace_2.0-2 nnet_7.3-16 withr_2.4.3 tidyselect_1.1.1 compiler_4.1.2
[16] cli_3.1.0 rvest_1.0.2 xml2_1.3.3 labeling_0.4.2 scales_1.1.1
[21] Peptides_2.4.4 digest_0.6.29 rmarkdown_2.13 pkgconfig_2.0.3 htmltools_0.5.2
[26] parallelly_1.30.0 dbplyr_2.1.1 fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1
[31] rstudioapi_0.13 farver_2.1.0 generics_0.1.1 jsonlite_1.7.2 ModelMetrics_1.2.2.2
[36] car_3.0-12 magrittr_2.0.1 Matrix_1.3-4 Rcpp_1.0.8 munsell_0.5.0
[41] fansi_1.0.2 abind_1.4-5 lifecycle_1.0.1 stringi_1.7.6 yaml_2.2.1
[46] carData_3.0-5 MASS_7.3-54 plyr_1.8.6 recipes_0.1.17 grid_4.1.2
[51] parallel_4.1.2 listenv_0.8.0 crayon_1.4.2 haven_2.4.3 splines_4.1.2
[56] hms_1.1.1 knitr_1.37 pillar_1.6.4 ggsignif_0.6.3 future.apply_1.8.1
[61] reshape2_1.4.4 codetools_0.2-18 stats4_4.1.2 reprex_2.0.1 glue_1.6.0
[66] evaluate_0.14 data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[71] foreach_1.5.1 cellranger_1.1.0 gtable_0.3.0 future_1.23.0 assertthat_0.2.1
[76] xfun_0.30 gower_0.2.2 prodlim_2019.11.13 broom_0.7.11 rstatix_0.7.0
[81] class_7.3-19 survival_3.2-13 timeDate_3043.102 iterators_1.0.13 lava_1.6.10
[86] globals_0.14.0 ellipsis_0.3.2 ipred_0.9-12
```