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https://github.com/fpaskali/lfapp
The repository includes the development version of R package LFApp
https://github.com/fpaskali/lfapp
calibration image-processing medical-image-processing r r-package rstats shiny
Last synced: 3 days ago
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The repository includes the development version of R package LFApp
- Host: GitHub
- URL: https://github.com/fpaskali/lfapp
- Owner: fpaskali
- Created: 2021-03-15T13:00:40.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-27T13:56:04.000Z (4 months ago)
- Last Synced: 2024-10-02T02:53:13.258Z (3 days ago)
- Topics: calibration, image-processing, medical-image-processing, r, r-package, rstats, shiny
- Language: R
- Homepage:
- Size: 14 MB
- Stars: 3
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# 
Shiny Apps for Lateral Flow Assays
The repository includes the development version of R package LFApp
[](https://www.gnu.org/licenses/lgpl-3.0)
[](https://www.repostatus.org/#active)
## Description
The LFA shiny apps include in package LFApp consist of four modular Shiny
applications:
(1) **LFA App core** for image acquisition, editing, region of interest definition
via gridding, background correction with multiple available methods, as well as
intensity data extraction of the pre-defined bands of the analysed LFAs. More
precisely, it consists of Tab 1, Tab 2 and parts of Tab 3 described in detail
below.
(2) **LFA App calibration** extends the LFA App core by methods for merging the
intensity data with information from experiments, computation of calibration
models and the generation of a report about the calibration analysis. The
functionality corresponds to the Tabs 1-6 described below.
(3) **LFA App quantification** enables quantification of the extracted intensity
values via loading existing calibration models. It extends the LFA App core
by Tab 7 described below.
(4) **LFA App analysis** includes the full functionality mentioned above and
enables full analysis in one application. That is, it consists of Tab 1-7.
The graphical user interface of the apps is built in a modular way divided into
several tabs, where each tab represents a specific step of the workflow. While
the applications can be used in a sequential fashion, the specific steps can
also be carried out individually.
## Testing our apps: shinyapps.io
Our apps can also be tested on shinyapps.io. The desktop version of our full
purpose analysis app is at
https://lfapp.shinyapps.io/LFAnalysis/
The mobile version is at
https://lfapp.shinyapps.io/mobile_app/
## Installation
The package requires Bioconductor package EBImage, which should be installed
first via
```{r}
## Install package BiocManager
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## Use BiocManager to install EBImage
BiocManager::install("EBImage", update = FALSE)
```
For generating our vignette and automatic reports, we need packages knitr and
rmarkdown, which will be installed next.
```{r}
## Install package knitr
if(!requireNamespace("knitr", quietly = TRUE))
install.packages("knitr")
## Install package rmarkdown
if(!requireNamespace("rmarkdown", quietly = TRUE))
install.packages("rmarkdown")
```
Finally, one can install package LFApp, where all remaining dependencies will
be installed automatically.
```{r}
## Install package LFApp
remotes::install_github("fpaskali/LFApp", build_vignette=TRUE)
```
## Start Apps
LFApp consist of four different shiny apps where there is a desktop and a
mobile version for each app. They can be started with one of the following
commands:
```{r}
## desktop versions
## LFA App core
LFApp::run_core()
## LFA App quantification
LFApp::run_quan()
## LFA App calibration
LFApp::run_cal()
## LFA App full analysis
LFApp::run_analysis()
## mobile versions
## LFA App core
LFApp::run_mobile_core()
## LFA App quantification
LFApp::run_mobile_quan()
## LFA App calibration
LFApp::run_mobile_cal()
## LFA App full analysis
LFApp::run_mobile_analysis()
```
## Open User's Guide
A comprehensive user's guide is included in our package in form of a so-called
vignette and can be opened via
```{r}
vignette("LFApp")
```
You can also find it at https://fpaskali.github.io/LFApp/
## YouTube Videos
There is a playlist at https://www.youtube.com/playlist?list=PLRgOZXM8LZ0gJwtsFNxBiu9WJG1TJjFuP