vignettes/tutorial_visualization.md

In ELELAB/lipidQ: Lipidomics Analysis Tool

title: "Tutorial: Visualization" author: "André Vidas Olsen" date: "2018-04-15" output: html_document vignette: > %\VignetteIndexEntry{Vignette Title} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

Introduction

This tutorial will cover all the basic steps involved in lipidQ visualization.

Data

The data for this tutorial will consist of the final output file containing the mol% pmol of species and classes.

Step 1: open LipidQ

Start by open up LipidQ. This can be done by doing these 3 steps:

1. Open Rstudio

2. Write "library(lipidQ)"

3. Write "runLipidQ()"

If the web browser encounters problems doing any of the steps in the tutorial, LipidQ can also be open by going to:

#> [1] "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/lipidQ/app/"

, open either ui.R or server.R and hit the Run button at the right top corner of Rstudio.

After this go to the visualization procedure tab.

Step 2: load data into LipidQ

In the visualization procedure tab section, start by choosing the input file to be used by clicking on the "Browse..." button below "Choose a mol% final output file (.csv-file)". For this tutorial we will be using the following two files:

1. finalOutput_molPct.csv

2. sampleTypes.csv

The two file is located in the following folder:

#> [1] "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/lipidQ/extdata/"

Go to this folder and select the two files.

Step 3: choose sample type file

Next, we need to select the file containing information about the sample types. This is done by clicking on the "Browse..." button below "Choose a file containing sample type information for the data".

Step 4: log2 transformation

Next, we'll choose to log2 transform our data by clicking on the checkbox "log2 transformation of the data". We'll also set the pseudo counts added before the transformation to 0.0001 to avoid $-\infty$ values in the transformed data.

Step 5: Select plot types

We now have to select the plot types that we want. In this analysis we're both interested in a PCA biplot and a heatmap, so we click on both the "Create PCA biplots of mol% classes and species" and the "Create heatmap plots of mol% classes and species" checkboxes.

Step 6: clustering

Since we chose to create a heatmap which contains clustering, a specification of the amount of clusters is needed. In our case we'll not let any prior knowledge about the sample types determine the amount of clusters, so we'll not write any number in the "Optional selection of k clusters for the heatmap" field. In this way we'll let the NbClust algortihm determine the most likely amount of clusters for the heatmap.

Step 7: Output folder

Lastly, the folder where the output files should be saved to has to be defined. Here we'll write the filepath to where we wish to save the files. The following describes how to obtain the full path:

From Mac OS X El Capitan 2015 onwards (As of February 2018): Go to the folder of interest, right click on the folder, press the alt button + click on 'Copy "folder-name" as Pathname'. Go back to LipidQ and paste the folder path (⌘ Command button + V) into the output folder field.

Windows: Go to the folder of interest, right click on the folder, go to properties, find Location and copy the path there (Ctrl + C). Go back to LipidQ and paste the full path (Ctrl + V) into the output folder field. Remember to change every "\" in the path to "/".

After this we'll press "Create Plots".

PCA plots

From the visualization process is done, we can go and have a look at the plots in the folder that we saved these in.

ELELAB/lipidQ documentation built on Feb. 24, 2020, 12:54 a.m.