Viewing Multiple Interactive Plots Using Plotly And Trelliscopejs
From R/Medicine Conference 2022
Jeremy John Selva, Research Assistant at National University of Singapore
Background: In a lipidomics analysis workflow, quality checks are done to evaluate a transition/compound measured. This results in many quality control (QC) plots created for each transition and output as a pdf file. However, as technologies improved, many transitions can be measured in one sample quickly. Today, over 500 transitions can be easily measured on large studies of a few thousand samples. Looking at several 500-page pdf files of such static plots has its limitations. Thus, a different approach is required. For each transition, R package {Plotly} was used to create these quality control plots like Injection Sequence vs Peak Area scatter plot and Raincloud plots (or violin plot). R package manipulateWidget is used to combine different Plotly plots together. Given a table with one Transition per row, these Plotly plots can be saved as a new column. R package Trelliscopejs is then used to view them. QC statistics and other metadata can be converted to cognostics which can be used to filter the plots and provide additional information. Trelliscopejs objects can be output as a non-self-contained HTML file which can be compressed in a folder and distributed to others.
Main Sections
0:00 Introduction
0:58 How lipids are measured (Target Lipidomics)
1:33 Source of “unwanted” variation
2:00 Quality control (QC) samples
2:38 Quality control plot
3:06 Motivation for using plotly and trelliscopejs
4:05 Moving in the right direction
5:58 Quarto example
6:19 Column of plots
6:44 trelliscopejs cognostics
7:15 Results distribution
8:27 Enjoy the conference!
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