Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/hjzollner/specvis

Repository of R-functions to visualize quantitative results from different linear-combination algorithms.
https://github.com/hjzollner/specvis

Last synced: 3 months ago
JSON representation

Repository of R-functions to visualize quantitative results from different linear-combination algorithms.

Host: GitHub
URL: https://github.com/hjzollner/specvis
Owner: HJZollner
License: mit
Created: 2020-05-11T20:31:37.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2023-06-01T19:58:06.000Z (over 1 year ago)
Last Synced: 2023-10-20T20:13:47.622Z (about 1 year ago)
Language: R
Homepage:
Size: 39.3 MB
Stars: 4
Watchers: 2
Forks: 4
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        # SpecVis

[![GitHub release (latest by date)](https://img.shields.io/github/release-date/HJZollner/SpecVis)](https://github.com/HJZollner/SpecVis/releases)

[![GitHub last commit](https://img.shields.io/github/last-commit/HJZollner/SpecVis)](https://github.com/HJZollner/SpecVis/commits/develop)

[![Website](https://img.shields.io/website?down_color=lightgrey&down_message=offline&up_color=green&up_message=online&url=https%3A%2F%2Fhjzollner.github.io)](https://hjzollner.github.io)

[![License](https://img.shields.io/github/license/HJZollner/SpecVis)](https://github.com/HJZollner/SpecVis/blob/develop/LICENSE.md)



SpecVis is a repository of R functions to visualize quantitative MRS results from different linear-combination algorithms.

# Getting started

## Prerequisites

SpecVis was developed in [RStudio (Version 1.2.5019)](https://rstudio.com/) on macOS Catalina (Version 10.15.3 (19D76)) and

the needed libraries are downloaded and installed automatically.

For high-resolution pdf files you need to have a functioning cairo_pdf device. You can always change the ggsave output format to any other format. 

## Installation

Download the latest **SpecVis** code from its [GitHub

repository](https://github.com/hezoe100/SpecVis), then include the SpecVis folder as

workdir (setwd()). Make sure to regularly check for updates, as we frequently commit new features, bug fixes, and improved

functions.

## Example markdown and script

An example [markdown](https://github.com/hezoe100/SpecVis/blob/master/examples/Example.md) is included in the repository. You can adapt your own script based on this [function](https://github.com/hezoe100/SpecVis/blob/master/examples/LC_comp.R).

# Features

- Load LCM-native result files from Osprey (.csv), LCModel (.coord), and Tarquin (.csv).

- Load statistics .csv-files which include group variables and correlation measures

- Raincloud plots (https://wellcomeopenresearch.org/articles/4-63) with individual datapoints, boxplots, distributions, and mean +- SD representations.

- Boxplots with individual datapoints

- Correlation plots with collapsed-correlations, group-level correlations, and indicators for sub-groups.

- Correlation plots with group-level facets and correlations for sub-groups.

- Bland-Altman plots with distribution ellipse with collapsed-distribution and group-distributions.

- Statistics script which automatically performs appropriate statistics, including descriptive statistics, tests for normality, variance analysis, and post hoc tests.

## Supported file formats

- Osprey .csv-files

- LCModel .coord-files

- Tarquin .csv-files

- arbitrary .csv-files

# Example plots

### Raincloud plot (https://wellcomeopenresearch.org/articles/4-63)

![Raincloud](/examples/RaincloudByLCMwithLimits4colums.png)

### Correlation plot

![Correlation](/examples/CorrelationByVendorWithSubgroups.png)

### Bland Altmann plot

![BlandAltmann](/examples/BlandAltmann.png)

### Correlation facet plot

![CorrelationFacet](/examples/CorrelationFacetSmall.png)

## Planned features

- Integration of spectra visualization

# Contact, Feedback, Suggestions

For any sort of questions, feedback, suggestions, or critique, please reach out to us via [email protected]. We also welcome your direct contributions to SpecVis here in the GitHub repository.

# Developers

- [Helge J. Zöllner](mailto:[email protected])

- [Georg Oeltzschner](mailto:[email protected])

Should you publish material that made use of SpecVis, please cite the following publication:

[HJ Zöllner, Michal Považan, SCN Hui, S Tapper, RAE Edden, Georg Oeltzschner, Agreement between different linear-combination modelling algorithms for short-TE proton spectra. bioRxiv 2020.](https://doi.org/10.1101/2020.06.05.136796)

Should you publish material that made use of the Raincloud plot script, please additionally cite: 

[Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res 2019, 4:63](https://wellcomeopenresearch.org/articles/4-63)

## Acknowledgements

We wish to thank Martin Wilson (University of Birmingham, Birmingham) for shared import code from the 'spant' R-package https://martin3141.github.io/spant/index.html. This code builds on modified version of the raincloud plots by Davide      Poggiali https://github.com/RainCloudPlots/RainCloudPlots.

This work has been supported by NIH grants R01EB016089, P41EB15909, R01EB023963, and K99AG062230.