https://github.com/biocomputingup/cagi-p16-assessment
https://genomeinterpretation.org/content/predict-how-variants-p16-tumor-suppressor-protein-affect-cell-proliferation
https://github.com/biocomputingup/cagi-p16-assessment
Last synced: 8 months ago
JSON representation
https://genomeinterpretation.org/content/predict-how-variants-p16-tumor-suppressor-protein-affect-cell-proliferation
- Host: GitHub
- URL: https://github.com/biocomputingup/cagi-p16-assessment
- Owner: BioComputingUP
- Created: 2017-04-07T11:05:30.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-04-14T10:25:04.000Z (about 9 years ago)
- Last Synced: 2025-07-13T06:41:16.201Z (11 months ago)
- Language: R
- Size: 25.4 KB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CAGI-p16-assessment
## Overview
This repository contains all the R scrips used for the assessment of the CAGI-3 ["p16 challenge"](https://genomeinterpretation.org/content/predict-how-variants-p16-tumor-suppressor-protein-affect-cell-proliferation).
All the scripts can be reused for performance assessment of bioinformatics tools to predict phenotypic effects of genetic variants of unknown significance (VUS).
## Dependencies
The analysis requires the following R packages to be installed
* [ROCR](https://cran.r-project.org/web/packages/ROCR/index.html)
* [plotrix](https://cran.r-project.org/web/packages/plotrix/index.html)
## Usage
Each script can be run from a terminal as the example below
```
Rscript 1_main_numerical_assessment.R
```
## Details
In this section a brief description of each script is given
Script | Description
------------ | -------------
1 | calculates the main numerical measures (i.e. correlations, AUC, RMSD).In additions it produces the tables needed by other scripts to generate assessment figures and tables.
2 | calculates correlation measure among all predictions and produce an heatmap figure to visualize results
3 | calculates correlation measure among performance indices and produce an heatmap figure to visualize results
4 | calculates the pairwise significance of challenge evaluations and produce an heatmap figure
5 | draws experimental values versus predicted values graph
6 | calculates only PSWD10 and produce a table to identify difficut targets