ShinyLipids

ShinyLipids is an interactive web application for lipidomics data analysis written in R with the shiny framework.

Getting started

First, we need to make sure that you have the latest version of R (and optionally RStudio) installed. If you are new to R, head over to https://cran.r-project.org/ and follow the download link suitable for your operating system. Then install RStudio from https://rstudio.com/products/rstudio/download/#download. Don’t worry, there won’t be any coding necessary to get started with your own data in ShinyLipids.

Now open up RStudio and run this piece of R code to install ShinyLipids with all it’s dependencies from github (To run the code, copy and paste it into the R console that pops up in the bottom right corner when you first open RStudio. Then hit Enter.):

if ( !("remotes" %in% installed.packages()) ) install.packages("remotes")
remotes::install_github("jmbuhr/ShinyLipids")

To get a first glimpse, run these lines to see the example data the comes with ShinyLipids:

path <- system.file("extdata/exampleDatabase.db", package = "ShinyLipids")
databaseConnection <- DBI::dbConnect(RSQLite::SQLite(), path)
ShinyLipids::run_app(db = databaseConnection)

Getting your own data into ShinyLipids

ShinyLipids loads the data from a database or a database-dump file as in the example above (exampleDatabase.db). This database needs to contain at least the two tables id_info and data2 [1]. Of course, you might not have your data in a database or database-dump file. To get your tabular data (preferably .csv) into such a file, you can use a little helper function in ShinyLipids, which you can run from an R script. Before you do so, make sure your tables contain the columns listed in section What should my tables look like?. Then, read them into R and package them up with ShinyLipids::createDatabase. This could look like this:

if ( !("readr" %in% installed.packages()) ) install.packages("readr")
path <- "path/to/your/data.db"
myMedatdata <- readr::read_csv("myMetadata.csv")
myDataset   <- readr::read_csv("myDataset.csv")
ShinyLipids::createDatabase(path = path, myMedatdata, myDataset)

Now you are ready to look at your lipids! Replace "path/to/your/data.db" with the actual filepath to your database-dump file and run:

databaseConnection <- DBI::dbConnect(RSQLite::SQLite(), path)
ShinyLipids::run_app(db = databaseConnection)

What should my tables look like?

id_info

This is myMetadata.csv in the previous example. Not all columns are needed but id and title are essential.

Column	Description
id	whole number: unique id per dataset
title	text: title of dataset
date_upload	text: YYYY-MM-DD
status	text: e.g. in progress
sample_from	text: owner of sample
date_sample	text: YYYY-MM-DD
extracted_by	text: name
date_extraction	text: YYYY-MM-DD
measured_by	text: name
date_measured	text: YYYY-MM-DD
distinct_samples	whole number: n of samples
data_lines	whole number: n of datapoints
file	text: name of raw datafile
instruments	text: instruments

data2

This is myDataset.csv.

Column	Description
id	whole number: matches metadata
lipid	text: format: “Cer 34:1;2”
value	number: (assumed to be µM)
category	text: e.g. SP
func_cat	text: e.g. SP
sample_identifier	text: e.g. “1Aa”
sample	text: e.g. “1 - 0min Stim”
sample_replicate	text: e.g. “1 - 0min Stim_A”
sample_replicate_technical	text: e.g. “1 - 0min Stim_A_a”

The lipid column should uniquely identify each measured lipid. If you use the nomenclature proposed by Liebisch et al. (https://www.ncbi.nlm.nih.gov/pubmed/23549332), ShinyLipids can extract further information about the lipid from this column (such as the length of the chains, the number of double bonds, the amount of hydroxylation etc.). The value is expected to be in µM. Using different units works but the axis labels will still read µM.

Optionally, your database can contain a table named “LIPID_CLASS_ORDER_COMPLETE” with a numeric class_order column associated with a class column.

For advanced use

If you want to run ShinyLipids on your own server, connected to a database or post your findings to shinyapps.io, you will have to download the source code by either running git clone https://github.com/jmbuhr/ShinyLipids.git from the command line or by visiting https://github.com/jmbuhr/ShinyLipids

Open the file app.R, read the comments and uncomment the specified lines. Then can to run the app or use the buttons in the top right corner of your RStudio code panel.

You can find further information in the documentation.

Notes

The first version of ShinyLipids was developed in 2015 by Mathias Gerl and further enhanced by Manuel Haußmann and Sebastian Bender until August, 2017. The ideas and intentions of this new version by Jannik Buhr are still based on the original, but the code and data structure is designed to be more consistent and host a safer environment for the implementation of new features. You can find more information about that in the documentation.

Please note that the ‘ShinyLipids’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

[1] the naming is due to back-end compatibility