https://github.com/noedemange/orderedheatmapanalysis

OrderedHeatMapAnalysis (OHMA) is a direct data analysis framework allowing to simultaneously visualize and analyze the structure of complex datasets. An optimized seriation of rows and columns of the input data table is performed, resulting in a mapping of the whole dataset into an ordered heatmap.
https://github.com/noedemange/orderedheatmapanalysis

analysis bi-seriation data dataanalysis heatmap r rstats seriation shiny shiny-apps

Last synced: 15 days ago
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OrderedHeatMapAnalysis (OHMA) is a direct data analysis framework allowing to simultaneously visualize and analyze the structure of complex datasets. An optimized seriation of rows and columns of the input data table is performed, resulting in a mapping of the whole dataset into an ordered heatmap.

• Host: GitHub
• URL: https://github.com/noedemange/orderedheatmapanalysis
• Owner: NoeDemange
• License: other
• Created: 2022-12-04T15:50:53.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-01T21:04:03.000Z (12 months ago)
• Last Synced: 2024-11-11T21:34:48.699Z (3 months ago)
• Topics: analysis, bi-seriation, data, dataanalysis, heatmap, r, rstats, seriation, shiny, shiny-apps
• Language: R
• Homepage:
• Size: 5.64 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# OrderedHeatMapAnalysis

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

OrderedHeatMapAnalysis (OHMA) is a direct data analysis framework allowing to simultaneously visualize and analyze the structure of complex datasets. An optimized seriation of rows and columns of the input data table is performed, resulting in a mapping of the whole dataset into an ordered heatmap. Following analysis of the ordered heatmap structure directly highlights submatrix of regularly ordered data. Subsequently, an exhaustive identification of biculsters laying in the subspaces of the dataset can be performed, and their mutual relationships can easily be characterized. This method allows a straitforwrard and deep exploration of all dimensions of the dataset.

## Installation

You can install the development version of OrderedHeatMapAnalysis from [GitHub](https://github.com/) with:

``` r

# install.packages("devtools")

devtools::install_github("NoeDemange/OrderedHeatMapAnalysis")

```

## Example

This is a basic example which shows you how to run the app:

```{r example}

library("OrderedHeatMapAnalysis")

OrderedHeatMapAnalysis::run_app(options=list("launch.browser"=TRUE))

```

## Credits

This app was developed by [Noe Demange](https://github.com/NoeDemange).

Contact the maintainer of the app, [Guillaume Sapriel](https://orcid.org/0000-0003-0549-9376). It is deployed on the

[MIGALE platform](https://migale.inrae.fr/) by [Cédric Midoux](https://orcid.org/0000-0002-7964-0929). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10.15454/1.5572390655343293E12) for providing help and storage resources.