visxhclust

visxhclust: An R Shiny package for visual exploration of hierarchical clustering - Published in JOSS (2022)

https://github.com/rhenkin/visxhclust

Keywords

Repository

A Shiny app and functions for visual exploration of hierarchical clustering.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# visxhclust: visual exploration of hierarchical clustering

[![R-CMD-check](https://github.com/rhenkin/visxhclust/workflows/R-CMD-check/badge.svg)](https://github.com/rhenkin/visxhclust/actions)
[![CRAN status](https://www.r-pkg.org/badges/version/visxhclust)](https://CRAN.R-project.org/package=visxhclust)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04074/status.svg)](https://doi.org/10.21105/joss.04074)


visxhclust is a package that includes a Shiny application for **vis**ual e**x**ploration of **h**ierarchical **clust**ering. It is aimed at facilitating iterative workflows of hierarchical clustering on numeric data. For that, the app allows users to quickly change parameters and analyse and evaluate results with typical heatmaps with dendrograms and other charts. Additionally, it includes lightweight data overview plots such as correlation heatmaps, annotated MDS and PCA plots. On the evaluation side, it builds on existing packages to compute internal validation scores and Gap statistic, as well as Dunn's test to evaluate significant differences between clusters. Many of the functions are also exported to facilitate documenting a complete analysis cycle.

**NEW!** A live demo of the app is running [here](https://rhenkin.shinyapps.io/visxhclust).

## Installation

The latest release can be installed from CRAN:

```{r cran, eval = FALSE}
install.packages("visxhclust")
```

The latest development version can be installed from GitHub:

```{r installation, eval = FALSE}
remotes::install_github("rhenkin/visxhclust")
```

Most dependencies are found in CRAN. However, the heatmap drawing package is part of [Bioconductor](http://www.bioconductor.org/) and may require a separate installation:

```{r bioconductor, eval = FALSE}
install.packages("BiocManager")
BiocManager::install("ComplexHeatmap")
```

## Getting started

To run the app once the package is installed, use the following commands:

```{r example, eval = FALSE}
library(visxhclust)
# Increases max file size to 30 MB
options(shiny.maxRequestSize = 30*1024^2)
run_app()
```

The app includes multiple help points in the interface (look for the question marks), and there are also three guides on how to use tool:

- An [animated guide](https://rhenkin.github.io/visxhclust/articles/visxhclust.html) on loading data and the basic clustering loop. It's also accessible in R by using the command `vignette("visxhclust")`.
- An example of how to [reproduce an analysis](https://rhenkin.github.io/visxhclust/articles/clusterworkflow.html) an analysis using the functions exported by the package. See with `vignette("clusterworkflow")` in R.
- An example of how to [reproduce the evaluation workflow](https://rhenkin.github.io/visxhclust/articles/clusterevaluation.html) using the functions exported by the package. See with `vignette("clusterevaluation")` in R.

## Usage tips and data requirements

To use your data with the tool, you can save a data frame or tibble in an RDS file, or use comma or tab-delimited files, with .csv, .tsv or .txt extensions. The clustering method supported by the tool works only on numeric values; columns containing text will be set aside to annotate the heatmap if so desired. If a column named `ID` exists, it will be used as an internal identifier for rows.

Clustering requires complete datasets with no missing values, NULLs or NAs. If any column contains missing values, it will be set aside to be used as a heatmap annotation. Badly formatted data will also lead to unexpected results in the tool. As an alternative, imputation packages can be used to fill missing data and faulty rows (e.g. text in numeric columns) should be removed before loading the file into the tool. The tool provides limited abilities to help with diagnosing issues and preprocessing data.

# Contributing

Please see the [guide](https://github.com/rhenkin/visxhclust/blob/master/CONTRIBUTING.md) for code contribution and suggestions.

Owner

JOSS Publication

visxhclust: An R Shiny package for visual exploration of hierarchical clustering

Published

February 05, 2022

DOI

10.21105/joss.04074

Volume 7, Issue 70, Page 4074

Authors

Rafael Henkin
Centre for Translational Bioinformatics, William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London

Michael R. Barnes
Centre for Translational Bioinformatics, William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London

Editor

Øystein Sørensen

Tags

clustering interactive visualization visual analytics Shiny

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